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Ajlouni N, Özyavaş A, Takaoğlu M, Takaoğlu F, Ajlouni F. Medical image diagnosis based on adaptive Hybrid Quantum CNN. BMC Med Imaging 2023; 23:126. [PMID: 37710188 PMCID: PMC10500912 DOI: 10.1186/s12880-023-01084-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023] Open
Abstract
Hybrid quantum systems have shown promise in image classification by combining the strengths of both classical and quantum algorithms. These systems leverage the parallel processing power of quantum computers to perform complex computations while utilizing classical algorithms to handle the vast amounts of data involved in imaging. The hybrid approach is intended to improve accuracy and speed compared to traditional classical methods. Further research and development in this area can revolutionize the way medical images are classified and help improve patient diagnosis and treatment. The use of Conventional Neural Networks (CNN) for the classification and diagnosis of medical images using big datasets requires, in most cases, the use of special high-performance computing machines, which are very expensive and hard to access by most researchers. A new form of Machine Learning (ML), Quantum machine learning (QML), is being introduced as an emerging strategy to overcome this problem. A hybrid quantum-classical CNN uses both quantum and classical convolution layers designed to use a parameterized quantum circuit. This means that the computing model utilizes a quantum circuits approach to construct QML algorithms, which are then used to transform the quantum state to extract image hidden features. This computational acceleration is expected to achieve better algorithm performance than classical CNNs. This study intends to evaluate the performance of a Hybrid Quantum CNN (HQCNN) against a conventional CNN. This is followed by some optimizer modifications for both proposed and classical CNN methods to investigate the possible further improvement of their performance. The optimizer modification is based on forcing the optimizer to be directly adaptive to model accuracy. The optimizer adaptiveness is based on the development of an optimizer with a loss base adaptive momentum. Several algorithms are developed to achieve the above-mentioned goals, including CNN, QCNN, CNN with the adaptive optimizer, and QCNN with the Adaptive optimizer. The four algorithms are tested against a Kaggle brain dataset containing over 7000 samples. The test results show the hybrid quantum circuit algorithm outperformed the conventional CNN algorithm. The performance of both algorithms was further improved by using a fully adaptive SGD optimizer.
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Affiliation(s)
- Naim Ajlouni
- Faculty of Engineering, Istanbul Atlas University, 34295, Istanbul, Türkiye.
- Faculty of Engineering, Istanbul Atlas University, Hamidiye, Anadolu Cd. No:40, 34408, 34403, Kağıthane, Istanbul, Turkey.
- Tübitak Bilgem, Barış, 1802. Sk. No:1, 41400, Gebze, Kocaeli, Turkey.
- Lancashire College of Further Education, Appleby Street, Lancashire, BB1 3BL, Blackburn, UK.
| | - Adem Özyavaş
- Faculty of Engineering, Istanbul Atlas University, 34295, Istanbul, Türkiye
| | - Mustafa Takaoğlu
- The Scientific and Technological Research Council of Türkiye (TÜBİTAK), BİLGEM, Kocaeli, Türkiye
| | - Faruk Takaoğlu
- The Scientific and Technological Research Council of Türkiye (TÜBİTAK), BİLGEM, Kocaeli, Türkiye
| | - Firas Ajlouni
- Department of Computer Science, Lancashire College of Further Education, Accrington, BB5 OHJ, UK
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Ram S, Tang W, Bell AJ, Pal R, Spencer C, Buschhaus A, Hatt CR, diMagliano MP, Rehemtulla A, Rodríguez JJ, Galban S, Galban CJ. Lung cancer lesion detection in histopathology images using graph-based sparse PCA network. Neoplasia 2023; 42:100911. [PMID: 37269818 DOI: 10.1016/j.neo.2023.100911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 05/17/2023] [Indexed: 06/05/2023]
Abstract
Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-rasG12D lung cancer mouse model using precision/recall rates, Fβ-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms.
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Affiliation(s)
- Sundaresh Ram
- Departments of Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Wenfei Tang
- Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alexander J Bell
- Departments of Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ravi Pal
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Cara Spencer
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Charles R Hatt
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; Imbio LLC, Minneapolis, MN 55405, USA
| | - Marina Pasca diMagliano
- Departments of Surgery, and Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alnawaz Rehemtulla
- Departments of Radiology, and Radiation Oncology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jeffrey J Rodríguez
- Departments of Electrical and Computer Engineering, and Biomedical Engineering, The University of Arizona, Tucson, AZ 85721, USA
| | - Stefanie Galban
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Craig J Galban
- Departments of Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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Karancsi Z, Hagenaars SC, Németh K, Mesker WE, Tőkés AM, Kulka J. Tumour-stroma ratio (TSR) in breast cancer: comparison of scoring core biopsies versus resection specimens. Virchows Arch 2023:10.1007/s00428-023-03555-0. [PMID: 37198327 DOI: 10.1007/s00428-023-03555-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/29/2023] [Accepted: 04/27/2023] [Indexed: 05/19/2023]
Abstract
PURPOSE Tumour-stroma ratio (TSR) is an important prognostic and predictive factor in several tumour types. The aim of this study is to determine whether TSR evaluated in breast cancer core biopsies is representative of the whole tumour. METHOD Different TSR scoring methods, their reproducibility, and the association of TSR with clinicopathological characteristics were investigated in 178 breast carcinoma core biopsies and corresponding resection specimens. TSR was assessed by two trained scientists on the most representative H&E-stained digitised slides. Patients were treated primarily with surgery between 2010 and 2021 at Semmelweis University, Budapest. RESULTS Ninety-one percent of the tumours were hormone receptor (HR)-positive (luminal-like). Interobserver agreement was highest using 100 × magnification (κcore = 0.906, κresection specimen = 0.882). The agreement between TSR of core biopsies and resection specimens of the same patients was moderate (κ = 0.514). Differences between the two types of samples were most frequent in cases with TSR scores close to the 50% cut-off point. TSR was strongly correlated with age at diagnosis, pT category, histological type, histological grade, and surrogate molecular subtype. A tendency was identified for more recurrences among stroma-high (SH) tumours (p = 0.07). Significant correlation was detected between the TSR and tumour recurrence in grade 1 HR-positive breast cancer cases (p = 0.03). CONCLUSIONS TSR is easy to determine and reproducible on both core biopsies and in resection specimens and is associated with several clinicopathological characteristics of breast cancer. TSR scored on core biopsies is moderately representative for the whole tumour.
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Affiliation(s)
- Zsófia Karancsi
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary.
| | - Sophie C Hagenaars
- Department of Surgery, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Kristóf Németh
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary
| | - Wilma E Mesker
- Department of Surgery, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Anna Mária Tőkés
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary
| | - Janina Kulka
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary
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Rashmi R, Prasad K, Udupa CBK. Region-based feature enhancement using channel-wise attention for classification of breast histopathological images. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07966-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
AbstractBreast histopathological image analysis at 400x magnification is essential for the determination of malignant breast tumours. But manual analysis of these images is tedious, subjective, error-prone and requires domain knowledge. To this end, computer-aided tools are gaining much attention in the recent past as it aids pathologists and save time. Furthermore, advances in computational power have leveraged the usage of computer tools. Yet, usage of computer-aided tools to analyse these images is challenging due to various reasons such as heterogeneity of malignant tumours, colour variations and presence of artefacts. Moreover, these images are captured at high resolutions which pose a major challenge to designing deep learning models as it demands high computational requirements. In this context, the present work proposes a new approach to efficiently and effectively extract features from these high-resolution images. In addition, at 400x magnification, the characteristics and structure of nuclei play a prominent role in the decision of malignancy. In this regard, the study introduces a novel CNN architecture called as CWA-Net that uses a colour channel attention module to enhance the features of the potential regions of interest such as nuclei. The developed model is qualitatively and quantitatively evaluated on private and public datasets and achieved an accuracy of 0.95% and 0.96%, respectively. The experimental evaluation demonstrates that the proposed method outperforms state-of-the-art methods on both datasets.
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Cross-domain decision making based on TrAdaBoost for diagnosis of breast lesions. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10267-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Mann S, Bindal AK, Balyan A, Shukla V, Gupta Z, Tomar V, Miah S. Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification. BIOMED RESEARCH INTERNATIONAL 2022; 2022:6392206. [PMID: 35993044 PMCID: PMC9388317 DOI: 10.1155/2022/6392206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 07/18/2022] [Indexed: 11/18/2022]
Abstract
Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z-moments for extracting features from the NSCT-decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state-of-the-art approaches.
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Affiliation(s)
- Suman Mann
- Department of Information Technology, Maharaja Surajmal Institute of Technology, New Delhi, India
| | - Amit Kumar Bindal
- Department of Computer Science & Engineering, MM Engineering College, MMDU, Mullana, Ambala, India
| | - Archana Balyan
- Department of Electronics and Communication Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India
| | - Vijay Shukla
- Department of Computer Science & Engineering, Greater Noida Institute of Technology, Greater Noida, India
| | - Zatin Gupta
- School of Computing Science & Engineering, Galgotias University, Greater Noida, Gautam Buddh Nagar, Uttar Pradesh, India
| | - Vivek Tomar
- Department of Computer Science & Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad, Uttar Pradesh, India
| | - Shahajan Miah
- Department of EEE, Bangladesh University of Business and Technology (BUBT), Dhaka, Bangladesh
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Breast Cancer Histopathological Image Recognition Based on Pyramid Gray Level Co-Occurrence Matrix and Incremental Broad Learning. ELECTRONICS 2022. [DOI: 10.3390/electronics11152322] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
In order to recognize breast cancer histopathological images, this article proposed a combined model consisting of a pyramid gray level co-occurrence matrix (PGLCM) feature extraction model and an incremental broad learning (IBL) classification model. The PGLCM model is designed to extract the fusion features of breast cancer histopathological images, which can reflect the multiresolution useful information of the images and facilitate the improvement of the classification effect in the later stage. The IBL model is used to improve the classification accuracy by increasing the number of network enhancement nodes horizontally. Unlike deep neural networks, the IBL model compresses the training and testing time cost greatly by making full use of its single-hidden-layer structure. To our knowledge, it is the first attempt for the IBL model to be introduced into the breast cancer histopathological image recognition task. The experimental results in four magnifications of the BreaKHis dataset show that the accuracy of binary classification and eight-class classification outperforms the existing algorithms. The accuracy of binary classification reaches 91.45%, 90.17%, 90.90% and 90.73%, indicating the effectiveness of the established combined model and demonstrating the advantages in breast cancer histopathological image recognition.
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Bhende M, Thakare A, Pant B, Singhal P, Shinde S, Saravanan V. Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4609625. [PMID: 35800216 PMCID: PMC9256435 DOI: 10.1155/2022/4609625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 05/28/2022] [Accepted: 06/11/2022] [Indexed: 12/04/2022]
Abstract
Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep pathological information mining is proposed: constructing a sample selection strategy, screening high-quality samples across different mammography image datasets, and dealing with the scarcity of medical image samples from the perspective of data enhancement; mining the pathology contained in limited labeled models from shallow to deep information; and dealing with the shortage of medical image samples from the perspective of feature optimization. The multiview effective region gene optimization (MvERGS) algorithm is designed to refine the original image features, improve the feature discriminate and compress the feature dimension, better match the number of samples, and perform discriminate correlation analysis (DCA) on the advanced new features; in-depth cross-modal correlation between heterogeneous elements, that is, the deep pathological information, can be mined to describe the breast mass lesion area accurately. Based on deep pathological information and traditional classifiers, an efficient breast mass recognition model is trained to complete the classification of mammography images. Experiments show that the key technical indicators of the recognition model, including accuracy and AUC, are better than the mainstream baselines, and the overfitting problem caused by the scarcity of samples is alleviated.
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Affiliation(s)
- Manisha Bhende
- Marathwada Mitra Mandal's Institute of Technology, Pune, India
| | | | - Bhasker Pant
- Department of Computer Science & Engineering, Graphic Era Deemed to Be University, Dehradun, Uttarakhand 248002, India
| | - Piyush Singhal
- Department of Mechanical Engineering, GLA University, Mathura 281406, India
| | - Swati Shinde
- Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India
| | - V. Saravanan
- Department of Computer Science, College of Engineering and Technology, Dambi Dollo University, Dambi Dollo, Oromia Region, Ethiopia
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Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head. Diagnostics (Basel) 2022; 12:diagnostics12051152. [PMID: 35626307 PMCID: PMC9139754 DOI: 10.3390/diagnostics12051152] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/23/2022] [Accepted: 04/28/2022] [Indexed: 11/16/2022] Open
Abstract
Introduction and Background: Despite fast developments in the medical field, histological diagnosis is still regarded as the benchmark in cancer diagnosis. However, the input image feature extraction that is used to determine the severity of cancer at various magnifications is harrowing since manual procedures are biased, time consuming, labor intensive, and error-prone. Current state-of-the-art deep learning approaches for breast histopathology image classification take features from entire images (generic features). Thus, they are likely to overlook the essential image features for the unnecessary features, resulting in an incorrect diagnosis of breast histopathology imaging and leading to mortality. Methods: This discrepancy prompted us to develop DEEP_Pachi for classifying breast histopathology images at various magnifications. The suggested DEEP_Pachi collects global and regional features that are essential for effective breast histopathology image classification. The proposed model backbone is an ensemble of DenseNet201 and VGG16 architecture. The ensemble model extracts global features (generic image information), whereas DEEP_Pachi extracts spatial information (regions of interest). Statistically, the evaluation of the proposed model was performed on publicly available dataset: BreakHis and ICIAR 2018 Challenge datasets. Result: A detailed evaluation of the proposed model’s accuracy, sensitivity, precision, specificity, and f1-score metrics revealed the usefulness of the backbone model and the DEEP_Pachi model for image classifying. The suggested technique outperformed state-of-the-art classifiers, achieving an accuracy of 1.0 for the benign class and 0.99 for the malignant class in all magnifications of BreakHis datasets and an accuracy of 1.0 on the ICIAR 2018 Challenge dataset. Conclusion: The acquired findings were significantly resilient and proved helpful for the suggested system to assist experts at big medical institutions, resulting in early breast cancer diagnosis and a reduction in the death rate.
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Hu H, Qiao S, Hao Y, Bai Y, Cheng R, Zhang W, Zhang G. Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy. PLoS One 2022; 17:e0266973. [PMID: 35482728 PMCID: PMC9049370 DOI: 10.1371/journal.pone.0266973] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 03/30/2022] [Indexed: 11/19/2022] Open
Abstract
Pathological examination is the gold standard for breast cancer diagnosis. The recognition of histopathological images of breast cancer has attracted a lot of attention in the field of medical image processing. In this paper, on the base of the Bioimaging 2015 dataset, a two-stage nuclei segmentation strategy, that is, a method of watershed segmentation based on histopathological images after stain separation, is proposed to make the dataset recognized to be the carcinoma and non-carcinoma recognition. Firstly, stain separation is performed on breast cancer histopathological images. Then the marker-based watershed segmentation method is used for images obtained from stain separation to achieve the nuclei segmentation target. Next, the completed local binary pattern is used to extract texture features from the nuclei regions (images after nuclei segmentation), and color features were extracted by using the color auto-correlation method on the stain-separated images. Finally, the two kinds of features were fused and the support vector machine was used for carcinoma and non-carcinoma recognition. The experimental results show that the two-stage nuclei segmentation strategy proposed in this paper has significant advantages in the recognition of carcinoma and non-carcinoma on breast cancer histopathological images, and the recognition accuracy arrives at 91.67%. The proposed method is also applied to the ICIAR 2018 dataset to realize the automatic recognition of carcinoma and non-carcinoma, and the recognition accuracy arrives at 92.50%.
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Affiliation(s)
- Hongping Hu
- School of Science, North University of China, Taiyuan, China
| | - Shichang Qiao
- School of Science, North University of China, Taiyuan, China
| | - Yan Hao
- School of Information and Communication Engineering, North University of China, Taiyuan, China
| | - Yanping Bai
- School of Science, North University of China, Taiyuan, China
| | - Rong Cheng
- School of Science, North University of China, Taiyuan, China
| | - Wendong Zhang
- School of Instrument and Electronics, State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China
| | - Guojun Zhang
- School of Instrument and Electronics, State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China
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A review on machine learning techniques for the assessment of image grading in breast mammogram. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01546-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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12
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Classification of Breast Cancer Images by Implementing Improved DCNN with Artificial Fish School Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6785707. [PMID: 35242181 PMCID: PMC8888076 DOI: 10.1155/2022/6785707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/21/2022] [Accepted: 01/28/2022] [Indexed: 11/17/2022]
Abstract
Breast cancer is an important factor affecting human health. This issue has various diagnosis process which were evolved such as mammography, fine needle aspirate, and surgical biopsy. These techniques use pathological breast cancer images for diagnosis. Breast cancer surgery allows the forensic doctor to histologist to access the microscopic level of breast tissues. The conventional method uses an optimized radial basis neural network using a cuckoo search algorithm. Existing radial basis neural network techniques utilized feature extraction and reduction parts separately. It is proposed that it overcomes the CNN approach for all the feature extraction and classification process to reduce time complexity. In this proposed method, a convolutional neural network is proposed based on an artificial fish school algorithm. The breast cancer image dataset is taken from cancer imaging archives. In the preprocessing step of classification, the breast cancer image is filtered with the support of a wiener filter for classification. The convolutional neural network has set the intense data of an image and is used to remove the features. After executing the extraction procedure, the reduction process is performed to speed up the train and test data processing. Here, the artificial fish school optimization algorithm is utilized to give the direct training data to the deep convolutional neural network. The extraction, reduction, and classification of features are utilized in the single deep convolutional neural network process. In this process, the optimization technique helps to decrease the error rate and increases the performance efficiency by finding the number of epochs and training images to the Deep CNN. In this system, the normal, benign, and malignant tissues are predicted. By comparing the existing RBF technique with the cuckoo search algorithm, the presented model attains the outcome in the way of sensitivity, accuracy, specificity, F1 score, and recall.
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Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia. BIG DATA AND COGNITIVE COMPUTING 2022. [DOI: 10.3390/bdcc6010013] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer.
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Rashmi R, Prasad K, Udupa CBK. Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review. J Med Syst 2021; 46:7. [PMID: 34860316 PMCID: PMC8642363 DOI: 10.1007/s10916-021-01786-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/21/2021] [Indexed: 12/24/2022]
Abstract
Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.
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Affiliation(s)
- R Rashmi
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Keerthana Prasad
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
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R R, Prasad K, Udupa CBK. BCHisto-Net: Breast histopathological image classification by global and local feature aggregation. Artif Intell Med 2021; 121:102191. [PMID: 34763806 DOI: 10.1016/j.artmed.2021.102191] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 09/15/2021] [Accepted: 10/05/2021] [Indexed: 02/06/2023]
Abstract
Breast cancer among women is the second most common cancer worldwide. Non-invasive techniques such as mammograms and ultrasound imaging are used to detect the tumor. However, breast histopathological image analysis is inevitable for the detection of malignancy of the tumor. Manual analysis of breast histopathological images is subjective, tedious, laborious and is prone to human errors. Recent developments in computational power and memory have made automation a popular choice for the analysis of these images. One of the key challenges of breast histopathological image classification at 100× magnification is to extract the features of the potential regions of interest to decide on the malignancy of the tumor. The current state-of-the-art CNN based methods for breast histopathological image classification extract features from the entire image (global features) and thus may overlook the features of the potential regions of interest. This can lead to inaccurate diagnosis of breast histopathological images. This research gap has motivated us to propose BCHisto-Net to classify breast histopathological images at 100× magnification. The proposed BCHisto-Net extracts both global and local features required for the accurate classification of breast histopathological images. The global features extract abstract image features while local features focus on potential regions of interest. Furthermore, a feature aggregation branch is proposed to combine these features for the classification of 100× images. The proposed method is quantitatively evaluated on red a private dataset and publicly available BreakHis dataset. An extensive evaluation of the proposed model showed the effectiveness of the local and global features for the classification of these images. The proposed method achieved an accuracy of 95% and 89% on KMC and BreakHis datasets respectively, outperforming state-of-the-art classifiers.
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Affiliation(s)
- Rashmi R
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Keerthana Prasad
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India.
| | - Chethana Babu K Udupa
- Department of Pathology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India.
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Huang Y, Zheng S, Lai B. Analysis of the Mechanism of Breast Metastasis Based on Image Recognition and Ultrasound Diagnosis. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4452500. [PMID: 34671449 PMCID: PMC8523227 DOI: 10.1155/2021/4452500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/09/2021] [Accepted: 09/18/2021] [Indexed: 11/24/2022]
Abstract
Breast cancer is one of the cancers with the highest incidence among women. In the late stage, cancer cells may metastasize to a distance, causing multiple organ diseases, threatening the lives of patients. The detection of lymph node metastasis based on pathological images is a key indicator for the diagnosis and staging of breast cancer, and correct staging decisions are the prerequisite and basis for targeted treatment. At present, the detection of lymph node metastasis mainly relies on manual screening by pathologists, which is time-consuming and labor-intensive, and the diagnosis results are variable and subjective. The automatic staging method based on the panoramic image calculation of the sentinel lymph node of the breast proposed in this paper can provide a set of standardized, high-accuracy, and repeatable objective diagnosis results. However, it is very difficult to automatically detect and locate cancer metastasis areas in highly complex panoramic images of lymph nodes. This paper proposes a novel deep network training strategy based on the sliding window to train an automatic localization model of cancer metastasis area. The training strategy first trains the initial convolutional network in a small amount of data, extracts false-positive and false-negative image blocks, and uses manual screening combined with automatic network screening to reclassify the false-positive blocks to improve the class of negative categories. Using mammography, ultrasound, MRI, and 18F-FDG PET-CT examinations, the detection rate and diagnostic accuracy of primary cancers in the breast of patients with axillary lymph node metastasis as the first diagnosis were obtained. The detection rate and diagnostic accuracy of breast MRI for primary cancers in the breast are much higher than those of X-ray, ultrasound, and 18F-FDG PET-CT (all P values <0.001). Mammography, ultrasound, and PET-CT examinations showed no difference in the detection rate and diagnostic accuracy of primary cancers in the breast of patients with axillary lymph node metastasis as the first diagnosis. Breast MRI should be used as a routine examination for patients with axillary lymph node metastasis as the first diagnosis. The primary breast cancer in the first diagnosed patients with axillary lymph node metastasis is often presented as localized asymmetric compactness or calcification on X-ray; it often appears as small focal mass lesions and ductal lesions without three-dimensional space-occupying effect on ultrasound.
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Affiliation(s)
- Yihong Huang
- Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou, Fujian 350007, China
| | - Shuo Zheng
- Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou, Fujian 350007, China
| | - Baoyong Lai
- Third Affiliated Hospital of Beijing University of Chinese Medicine, Beijing 100029, China
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Ma D, Lu Z, Xia L, Liao Q, Yang W, Ma H, Liao R, Ma L, Liu Z. MuellerNet: a hybrid 3D-2D CNN for cell classification with Mueller matrix images. APPLIED OPTICS 2021; 60:6682-6694. [PMID: 34612912 DOI: 10.1364/ao.431076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Different from conventional microimaging techniques, polarization imaging can generate multiple polarization images in a single perspective by changing the polarization angle. However, how to efficiently fuse the information in these multiple polarization images by a convolutional neural network (CNN) is still a challenging problem. In this paper, we propose a hybrid 3D-2D convolutional neural network called MuellerNet, to classify biological cells with Mueller matrix images (MMIs). The MuellerNet includes a normal stream and a polarimetric stream, in which the first Mueller matrix image is taken as the input of normal stream, and the rest MMIs are stacked to form the input of a polarimetric stream. The normal stream is mainly constructed with a backbone network and, in the polarimetric stream, the attention mechanism is used to adaptively assign weights to different convolutional maps. To improve the network's discrimination, a loss function is introduced to simultaneously optimize parameters of the two streams. Two Mueller matrix image datasets are built, which include four types of breast cancer cells and three types of algal cells, respectively. Experiments are conducted on these two datasets with many well-known and recent networks. Results show that the proposed network efficiently improves the classification accuracy and helps to find discriminative features in MMIs.
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18
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Meng Z, Zhao Z, Li B, Su F, Guo L, Wang H. Triple Up-Sampling Segmentation Network With Distribution Consistency Loss for Pathological Diagnosis of Cervical Precancerous Lesions. IEEE J Biomed Health Inform 2021; 25:2673-2685. [PMID: 33296318 DOI: 10.1109/jbhi.2020.3043589] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Cervical cancer, as one of the most frequently diagnosed cancers in women, is curable when detected early. However, automated algorithms for cervical pathology precancerous diagnosis are limited. METHODS In this paper, instead of popular patch-wise classification, an end-to-end patch-wise segmentation algorithm is proposed to focus on the spatial structure changes of pathological tissues. Specifically, a triple up-sampling segmentation network (TriUpSegNet) is constructed to aggregate spatial information. Second, a distribution consistency loss (DC-loss) is designed to constrain the model to fit the inter-class relationship of the cervix. Third, the Gauss-like weighted post-processing is employed to reduce patch stitching deviation and noise. RESULTS The algorithm is evaluated on three challenging and public datasets: 1) MTCHI for cervical precancerous diagnosis, 2) DigestPath for colon cancer, and 3) PAIP for liver cancer. The Dice coefficient is 0.7413 on the MTCHI dataset, which is significantly higher than the published state-of-the-art results. CONCLUSION Experiments on the public dataset MTCHI indicate the superiority of the proposed algorithm on cervical pathology precancerous diagnosis. In addition, the experiments on two other pathological datasets, i.e., DigestPath and PAIP, demonstrate the effectiveness and generalization ability of the TriUpSegNet and weighted post-processing on colon and liver cancers. SIGNIFICANCE The end-to-end TriUpSegNet with DC-loss and weighted post-processing leads to improved segmentation in pathology of various cancers.
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Sun Y, Huang X, Zhou H, Zhang Q. SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images. Med Image Anal 2021; 72:102142. [PMID: 34198042 DOI: 10.1016/j.media.2021.102142] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 05/11/2021] [Accepted: 06/17/2021] [Indexed: 10/21/2022]
Abstract
The detection of nuclei and cells in histology images is of great value in both clinical practice and pathological studies. However, multiple reasons such as morphological variations of nuclei or cells make it a challenging task where conventional object detection methods cannot obtain satisfactory performance in many cases. A detection task consists of two sub-tasks, classification and localization. Under the condition of dense object detection, classification is a key to boost the detection performance. Considering this, we propose similarity based region proposal networks (SRPN) for nuclei and cells detection in histology images. In particular, a customised convolution layer termed as embedding layer is designed for network building. The embedding layer is added into the region proposal networks, enabling the networks to learn discriminative features based on similarity learning. Features obtained by similarity learning can significantly boost the classification performance compared to conventional methods. SRPN can be easily integrated into standard convolutional neural networks architectures such as the Faster R-CNN and RetinaNet. We test the proposed approach on tasks of multi-organ nuclei detection and signet ring cells detection in histological images. Experimental results show that networks applying similarity learning achieved superior performance on both tasks when compared to their counterparts. In particular, the proposed SRPN achieve state-of-the-art performance on the MoNuSeg benchmark for nuclei segmentation and detection while compared to previous methods, and on the signet ring cell detection benchmark when compared with baselines. The sourcecode is publicly available at: https://github.com/sigma10010/nuclei_cells_det.
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Affiliation(s)
- Yibao Sun
- School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom
| | - Xingru Huang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom.
| | - Huiyu Zhou
- School of Informatics, University of Leicester, University Road, Leicester, LE1 7RH, United Kingdom
| | - Qianni Zhang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom
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20
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Hao Y, Qiao S, Zhang L, Xu T, Bai Y, Hu H, Zhang W, Zhang G. Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features. Front Oncol 2021; 11:657560. [PMID: 34195073 PMCID: PMC8236881 DOI: 10.3389/fonc.2021.657560] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 05/11/2021] [Indexed: 11/28/2022] Open
Abstract
Breast cancer (BC) is the primary threat to women’s health, and early diagnosis of breast cancer is imperative. Although there are many ways to diagnose breast cancer, the gold standard is still pathological examination. In this paper, a low dimensional three-channel features based breast cancer histopathological images recognition method is proposed to achieve fast and accurate breast cancer benign and malignant recognition. Three-channel features of 10 descriptors were extracted, which are gray level co-occurrence matrix on one direction (GLCM1), gray level co-occurrence matrix on four directions (GLCM4), average pixel value of each channel (APVEC), Hu invariant moment (HIM), wavelet features, Tamura, completed local binary pattern (CLBP), local binary pattern (LBP), Gabor, histogram of oriented gradient (Hog), respectively. Then support vector machine (SVM) was used to assess their performance. Experiments on BreaKHis dataset show that GLCM1, GLCM4 and APVEC achieved the recognition accuracy of 90.2%-94.97% at the image level and 89.18%-94.24% at the patient level, which is better than many state-of-the-art methods, including many deep learning frameworks. The experimental results show that the breast cancer recognition based on high dimensional features will increase the recognition time, but the recognition accuracy is not greatly improved. Three-channel features will enhance the recognizability of the image, so as to achieve higher recognition accuracy than gray-level features.
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Affiliation(s)
- Yan Hao
- School of Information and Communication Engineering, North University of China, Taiyuan, China
| | - Shichang Qiao
- Department of Mathematics, School of Science, North University of China, Taiyuan, China
| | - Li Zhang
- Department of Mathematics, School of Science, North University of China, Taiyuan, China
| | - Ting Xu
- Department of Mathematics, School of Science, North University of China, Taiyuan, China
| | - Yanping Bai
- Department of Mathematics, School of Science, North University of China, Taiyuan, China
| | - Hongping Hu
- Department of Mathematics, School of Science, North University of China, Taiyuan, China
| | - Wendong Zhang
- School of Instrument and Electronics, Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China
| | - Guojun Zhang
- School of Instrument and Electronics, Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China
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21
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Sprenger J, Murray C, Lad J, Jones B, Thomas G, Nofech-Mozes S, Khorasani M, Vitkin A. Toward a quantitative method for estimating tumour-stroma ratio in breast cancer using polarized light microscopy. BIOMEDICAL OPTICS EXPRESS 2021; 12:3241-3252. [PMID: 34221657 PMCID: PMC8221948 DOI: 10.1364/boe.422452] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/28/2021] [Accepted: 05/01/2021] [Indexed: 05/10/2023]
Abstract
The tumour-stroma ratio (TSR) has been explored as a useful source of prognostic information in various cancers, including colorectal, breast, and gastric. Despite research showing potential prognostic utility, its uptake into the clinic has been limited, in part due to challenges associated with subjectivity, reproducibility, and quantification. We have recently proposed a simple, robust, and quantifiable high-contrast method of imaging intra- and peri-tumoural stroma based on polarized light microscopy. Here we report on its use to quantify TSR in human breast cancer using unstained slides from 40 patient samples of invasive ductal carcinoma (IDC). Polarimetric results based on a stromal abundance metric correlated well with pathology designations, showing a statistically significant difference between high- and low-stroma samples as scored by two clinical pathologists. The described polarized light imaging methodology shows promise for use as a quantitative, automatic, and standardizable tool for quantifying TSR, potentially addressing some of the challenges associated with its current estimation.
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Affiliation(s)
- Jillian Sprenger
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Ciara Murray
- Laboratory Medicine Program, University Health Network, Ontario, Canada
| | - Jigar Lad
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Blake Jones
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Georgia Thomas
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Sharon Nofech-Mozes
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Mohammadali Khorasani
- Department of Surgery, University of British Columbia, Victoria, Canada
- Co-senior authors
| | - Alex Vitkin
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Division of Biophysics and Bioimaging, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Co-senior authors
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22
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Xu J, Lu H, Li H, Yan C, Wang X, Zang M, Rooij DGD, Madabhushi A, Xu EY. Computerized spermatogenesis staging (CSS) of mouse testis sections via quantitative histomorphological analysis. Med Image Anal 2021; 70:101835. [PMID: 33676102 PMCID: PMC8046964 DOI: 10.1016/j.media.2020.101835] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 07/20/2020] [Accepted: 07/28/2020] [Indexed: 01/16/2023]
Abstract
Spermatogenesis in mammals is a cyclic process of spermatogenic cell development in the seminiferous epithelium that can be subdivided into 12 subsequent stages. Histological staging analysis of testis sections, specifically of seminiferous tubule cross-sections, is the only effective method to evaluate the quality of the spermatogenic process and to determine developmental defects leading to infertility. Such staging analysis, however, is tedious and time-consuming, and it may take a long time to become proficient. We now have developed a Computerized Staging system of Spermatogenesis (CSS) for mouse testis sections through learning of an expert with decades of experience in mouse testis staging. The development of the CSS system comprised three major parts: 1) Developing computational image analysis models for mouse testis sections; 2) Automated classification of each seminiferous tubule cross-section into three stage groups: Early Stages (ES: stages I-V), Middle Stages (MS: stages VI-VIII), and Late Stages (LS: stages IV-XII); 3) Automated classification of MS into distinct stages VI, VII-mVIII, and late VIII based on newly developed histomorphological features. A cohort of 40 H&E stained normal mouse testis sections was built according to three modules where 28 cross-sections were leveraged for developing tubule region segmentation, spermatogenic cells types and multi-concentric-layers segmentation models. The rest of 12 testis cross-sections, approximately 2314 tubules whose stages were manually annotated by two expert testis histologists, served as the basis for developing the CSS system. The CSS system's accuracy of mean and standard deviation (MSD) in identifying ES, MS, and LS were 0.93 ± 0.03, 0.94 ± 0.11, and 0.89 ± 0.05 and 0.85 ± 0.12, 0.88 ± 0.07, and 0.96 ± 0.04 for one with 5 years of experience, respectively. The CSS system's accuracy of MSD in identifying stages VI, VII-mVIII, and late VIII are 0.74 ± 0.03, 0.85 ± 0.04, and 0.78 ± 0.06 and 0.34 ± 0.18, 0.78 ± 0.16, and 0.44 ± 0.25 for one with 5 years of experience, respectively. In terms of time it takes to collect these data, it takes on average 3 hours for a histologist and 1.87 hours for the CSS system to finish evaluating an entire testis section (computed with a PC (I7-6800k 4.0 GHzwith 32GB of RAM & 256G SSD) and a Titan 1080Ti GPU). Therefore, the CSS system is more accurate and faster compared to a human histologist in staging, and further optimization and development will not only lead to a complete staging of all 12 stages of mouse spermatogenesis but also could aid in the future diagnosis of human infertility. Moreover, the top-ranking histomorphological features identified by the CSS classifier are consistent with the primary features used by histologists in discriminating stages VI, VII-mVIII, and late VIII.
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Affiliation(s)
- Jun Xu
- Jiangsu Key Laboratory of Big Data Analysis Technique and CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Haoda Lu
- Jiangsu Key Laboratory of Big Data Analysis Technique and CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Haixin Li
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Chaoyang Yan
- Jiangsu Key Laboratory of Big Data Analysis Technique and CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Xiangxue Wang
- Department of Biomedical Engineering, Case Western Reserve University, OH 44106-7207, USA
| | - Min Zang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Dirk G de Rooij
- Reproductive Biology Group, Division of Developmental Biology, Dept. of Biology, Faculty of Science, Utrecht University, Utrecht 3584 CH, The Netherlands; Center for Reproductive Medicine, Academic Medical Center, University of Amsterdam, Amsterdam 1105 AZ, The Netherlands
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, OH 44106-7207, USA; Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio 44106-7207, USA
| | - Eugene Yujun Xu
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, China; Department of Neurology, Center for Reproductive Sciences, Northwestern University Feinberg School of Medicine, IL 60611, USA.
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Rajathi GM. Optimized Radial Basis Neural Network for Classification of Breast Cancer Images. Curr Med Imaging 2021; 17:97-108. [PMID: 32416697 DOI: 10.2174/1573405616666200516172118] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 04/18/2020] [Accepted: 04/25/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Breast cancer is a curable disease if diagnosed at an early stage. The chances of having breast cancer are the lowest in married women after the breast-feeding phase because the cancer is formed from the blocked milk ducts. INTRODUCTION Nowadays, cancer is considered the leading cause of death globally. Breast cancer is the most common cancer among females. It is possible to develop breast cancer while breast-feeding a baby, but it is rare. Mammography is one of the most effective methods used in hospitals and clinics for early detection of breast cancer. Various researchers are used in artificial intelligence- based mammogram techniques. This process of mammography will reduce the death rate of the patients affected by breast cancer. This process is improved by the image analysing, detection, screening, diagnosing, and other performance measures. METHODS The radial basis neural network will be used for classification purposes. The radial basis neural network is designed with the help of the optimization algorithm. The optimization is to tune the classifier to reduce the error rate with the minimum time for the training process. The cuckoo search algorithm will be used for this purpose. RESULTS Thus, the proposed optimum RBNN is determined to classify breast cancer images. In this, the three sets of properties were classified by performing the feature extraction and feature reduction. In this breast cancer MRI image, the normal, benign, and malignant is taken to perform the classification. The minimum fitness value is determined to evaluate the optimum value of possible locations. The radial basis function is evaluated with the cuckoo search algorithm to optimize the feature reduction process. The proposed methodology is compared with the traditional radial basis neural network using the evaluation parameter like accuracy, precision, recall and f1-score. The whole system model is done by using Matrix Laboratory (MATLAB) with the adaptation of 2018a since the proposed system is most efficient than most recent related literature. CONCLUSION Thus, it concluded with the efficient classification process of RBNN using a cuckoo search algorithm for breast cancer images. The mammogram images are taken into recent research because breast cancer is a major issue for women. This process is carried to classify the various features for three sets of properties. The optimized classifier improves performance and provides a better result. In this proposed research work, the input image is filtered using a wiener filter, and the classifier extracts the feature based on the breast image.
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Affiliation(s)
- G M Rajathi
- Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India
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24
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Abstract
Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. Besides, we present a list of publicly available and private datasets that have been used in HI research.
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25
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Liu P, Fu B, Yang SX, Deng L, Zhong X, Zheng H. Optimizing Survival Analysis of XGBoost for Ties to Predict Disease Progression of Breast Cancer. IEEE Trans Biomed Eng 2020; 68:148-160. [PMID: 32406821 DOI: 10.1109/tbme.2020.2993278] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Some excellent prognostic models based on survival analysis methods for breast cancer have been proposed and extensively validated, which provide an essential means for clinical diagnosis and treatment to improve patient survival. To analyze clinical and follow-up data of 12119 breast cancer patients, derived from the Clinical Research Center for Breast (CRCB) in West China Hospital of Sichuan University, we developed a gradient boosting algorithm, called EXSA, by optimizing survival analysis of XGBoost framework for ties to predict the disease progression of breast cancer. METHODS EXSA is based on the XGBoost framework in machine learning and the Cox proportional hazards model in survival analysis. By taking Efron approximation of partial likelihood function as a learning objective for ties, EXSA derives gradient formulas of a more precise approximation. It optimizes and enhances the ability of XGBoost for survival data with ties. After retaining 4575 patients (3202 cases for training, 1373 cases for test), we exploit the developed EXSA method to build an excellent prognostic model to estimate disease progress. Risk score of disease progress is evaluated by the model, and the risk grouping and continuous functions between risk scores and disease progress rate at 5- and 10-year are also demonstrated. RESULTS Experimental results on test set show that the EXSA method achieves competitive performance with concordance index of 0.83454, 5-year and 10-year AUC of 0.83851 and 0.78155, respectively. CONCLUSION The proposed EXSA method can be utilized as an effective method for survival analysis. SIGNIFICANCE The proposed method in this paper can provide an important means for follow-up data of breast cancer or other disease research.
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The significance of stromal collagen organization in cancer tissue: An in-depth discussion of literature. Crit Rev Oncol Hematol 2020; 151:102907. [DOI: 10.1016/j.critrevonc.2020.102907] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 02/09/2020] [Accepted: 02/10/2020] [Indexed: 12/12/2022] Open
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27
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Jones B, Thomas G, Westreich J, Nofech-Mozes S, Vitkin A, Khorasani M. Novel quantitative signature of tumor stromal architecture: polarized light imaging differentiates between myxoid and sclerotic human breast cancer stroma. BIOMEDICAL OPTICS EXPRESS 2020; 11:3246-3262. [PMID: 32637252 PMCID: PMC7316019 DOI: 10.1364/boe.392722] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/12/2020] [Accepted: 05/14/2020] [Indexed: 05/02/2023]
Abstract
As a leading cause of death in women, breast cancer is a global health concern for which personalized therapy remains largely unrealized, resulting in over- or under-treatment. Recently, tumor stroma has been shown to carry important prognostic information, both in its relative abundance and morphology, but its current assessment methods are few and suboptimal. Herein, we present a novel stromal architecture signature (SAS) methodology based on polarized light imaging that quantifies patterns of tumor connective tissue. We demonstrate its ability to differentiate between myxoid and sclerotic stroma, two pathology-derived categories associated with significantly different patient outcomes. The results demonstrate a 97% sensitivity and 88% specificity for myxoid stroma identification in a pilot study of 102 regions of interest from human invasive ductal carcinoma breast cancer surgical specimens (20 patients). Additionally, the SAS numerical score is indicative of the wide range of stromal characteristics within these binary classes and highlights ambiguous mixed-morphology regions prone to misclassification. The enabling polarized light microscopy technique is inexpensive, fast, fully automatable, applicable to fresh or embedded tissue without the need for staining and thus potentially translatable into research and/or clinical settings. The SAS metric yields quantifiable and objective stromal characterization with promise for prognosis in many types of cancers beyond breast carcinoma, enabling researchers and clinicians to further investigate the emerging and important role of stromal architectural patterns in solid tumors.
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Affiliation(s)
- Blake Jones
- Department of Medical Biophysics, University of Toronto, 101 College St, Toronto, ON M5G 1L7, Canada
- Authors contributed equally
| | - Georgia Thomas
- Department of Medical Biophysics, University of Toronto, 101 College St, Toronto, ON M5G 1L7, Canada
- Authors contributed equally
| | - Jared Westreich
- Department of Medical Biophysics, University of Toronto, 101 College St, Toronto, ON M5G 1L7, Canada
| | - Sharon Nofech-Mozes
- Department of Laboratory Medicine and Pathobiology, University of Toronto, 1 King's College Cir, Toronto, ON M5S 1A8, Canada
| | - Alex Vitkin
- Department of Medical Biophysics, University of Toronto, 101 College St, Toronto, ON M5G 1L7, Canada
- Division of Biophysics and Bioimaging, Princess Margaret Cancer Center, University Health Network, 610 University Ave, Toronto, ON M5G 2C1, Canada
- Department of Radiation Oncology, University of Toronto, Stewart building, 149 College St Suite 504, Toronto, ON M5 T 1P5, Canada
- Co-senior authors
| | - Mohammadali Khorasani
- Department of Surgical Oncology, University of Toronto, Princess Margaret Cancer Center, OPG Wing, 6th floor, 610 University Avenue Toronto, ON M5G 2M9, Canada
- Co-senior authors
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Inge L, Dennis E. Development and applications of computer image analysis algorithms for scoring of PD-L1 immunohistochemistry. ACTA ACUST UNITED AC 2020; 6:2-8. [PMID: 35757235 PMCID: PMC9216464 DOI: 10.1016/j.iotech.2020.04.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Immune checkpoint inhibitors targeting programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) have rapidly become integral to standard-of-care therapy for non-small cell lung cancer and other cancers. Immunohistochemical (IHC) staining of PD-L1 is currently the accepted and approved diagnostic assay for selecting patients for PD-L1/PD-1 axis therapies in certain indications. However, the inherent biological complexity of PD-L1 and the availability of several PD-L1 assays – each with different detection systems, platforms, scoring algorithms and cut-offs – have created challenges to ensure reliable and reproducible results based on subjective visual assessment by pathologists. The increasing adoption of computer technologies into the daily workflow of pathology provides an opportunity to leverage these tools towards improving the clinical value of PD-L1 IHC assays. This review describes several image analysis software programs of computer-aided PD-L1 scoring in the hope of driving further discussion and technological advancement in digital pathology and artificial intelligence approaches, particularly as precision medicine evolves to encompass accurate simultaneous assessment of multiple features of cancer cells and their interactions with the tumor microenvironment.
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Nazir A, Cheema MN, Sheng B, Li P, Li H, Yang P, Jung Y, Qin J, Feng DD. SPST-CNN: Spatial pyramid based searching and tagging of liver's intraoperative live views via CNN for minimal invasive surgery. J Biomed Inform 2020; 106:103430. [PMID: 32371232 DOI: 10.1016/j.jbi.2020.103430] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 01/31/2020] [Accepted: 04/20/2020] [Indexed: 11/19/2022]
Abstract
Laparoscopic liver surgery is challenging to perform because of compromised ability of the surgeon to localize subsurface anatomy due to minimal invasive visibility. While image guidance has the potential to address this barrier, intraoperative factors, such as insufflations and variable degrees of organ mobilization from supporting ligaments, may generate substantial deformation. The navigation ability in terms of searching and tagging within liver views has not been characterized, and current object detection methods do not account for the mechanics of how these features could be applied to the liver images. In this research, we have proposed spatial pyramid based searching and tagging of liver's intraoperative views using convolution neural network (SPST-CNN). By exploiting a hybrid combination of an image pyramid at input and spatial pyramid pooling layer at deeper stages of SPST-CNN, we reveal the gains of full-image representations for searching and tagging variable scaled liver live views. SPST-CNN provides pinpoint searching and tagging of intraoperative liver views to obtain up-to-date information about the location and shape of the area of interest. Downsampling input using image pyramid enables SPST-CNN framework to deploy input images with a diversity of resolutions for achieving scale-invariance feature. We have compared the proposed approach to the four recent state-of-the-art approaches and our method achieved better mAP up to 85.9%.
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Affiliation(s)
- Anam Nazir
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, China
| | | | - Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, China.
| | - Ping Li
- Department of Computing, The Hong Kong Polytechnic University, Hong Kong
| | - Huating Li
- Sixth People's Hospital, Shanghai Jiao Tong University, China.
| | - Po Yang
- Department of Computer Science, University of Sheffield, UK
| | - Younhyun Jung
- School of Information Technologies, The University of Sydney, Australia
| | - Jing Qin
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong
| | - David Dagan Feng
- School of Information Technologies, The University of Sydney, Australia
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Krishnamurthi R, Aggrawal N, Sharma L, Srivastava D, Sharma S. Importance of Feature Selection and Data Visualization Towards Prediction of Breast Cancer. ACTA ACUST UNITED AC 2019. [DOI: 10.2174/2213275912666190101121058] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Breast cancer is one of the most common forms of cancers among women
and the leading cause of death among them. Countries like United States, England and Canada have
reported a high number of breast cancer patients every year and this number is continuously increasing
due to detection at later stages. Hence, it is very important to create awareness among women
and develop such algorithms which help to detect malignant cancer. Several research studies have
been conducted to analyze the breast cancer data.
Objective:
This paper presents an effective method in predicting breast cancer and its stage and will
also analyze the performance of different supervised learning algorithms such as Random Classifier,
Chi2 Square test used in order to predict. The paper focuses on the three important aspects such as
the feature selection, the corresponding data visualisation and finally making a prediction call on different
machine learning models.
Methods:
The dataset used for this work is breast cancer Wisconsin data taken from UCI library.
The dataset has been used to show the different 32 features which are all important and how it can
be achieved using data visualisation. Secondly, after the feature selection, different machine learning
models have been applied.
Conclusion:
The machine learning models involved are namely Support Vector Machine (SVM), KNearest
Neighbour (KNN), Random Forest, Principal Component Analysis (PCA), Neural Network
using Perceptron (NNP). This has been done to check which type of model is better under what conditions.
At different stages several charts have been plotted and eliminated based on relative comparison.
Results have shown that Random Tree classifier along with Chi2 Square proves to be an efficient
one.
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Affiliation(s)
- Rajalakshmi Krishnamurthi
- Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India
| | - Niyati Aggrawal
- Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India
| | - Lokendra Sharma
- Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India
| | - Diva Srivastava
- Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India
| | - Shivangi Sharma
- Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India
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Du Y, Zhang R, Zargari A, Thai TC, Gunderson CC, Moxley KM, Liu H, Zheng B, Qiu Y. Classification of Tumor Epithelium and Stroma by Exploiting Image Features Learned by Deep Convolutional Neural Networks. Ann Biomed Eng 2018; 46:1988-1999. [PMID: 30051247 PMCID: PMC6286645 DOI: 10.1007/s10439-018-2095-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 07/13/2018] [Indexed: 01/17/2023]
Abstract
The tumor-stroma ratio (TSR) reflected on hematoxylin and eosin (H&E)-stained histological images is a potential prognostic factor for survival. Automatic image processing techniques that allow for high-throughput and precise discrimination of tumor epithelium and stroma are required to elevate the prognostic significance of the TSR. As a variant of deep learning techniques, transfer learning leverages nature-images features learned by deep convolutional neural networks (CNNs) to relieve the requirement of deep CNNs for immense sample size when handling biomedical classification problems. Herein we studied different transfer learning strategies for accurately distinguishing epithelial and stromal regions of H&E-stained histological images acquired from either breast or ovarian cancer tissue. We compared the performance of important deep CNNs as either a feature extractor or as an architecture for fine-tuning with target images. Moreover, we addressed the current contradictory issue about whether the higher-level features would generalize worse than lower-level ones because they are more specific to the source-image domain. Under our experimental setting, the transfer learning approach achieved an accuracy of 90.2 (vs. 91.1 for fine tuning) with GoogLeNet, suggesting the feasibility of using it in assisting pathology-based binary classification problems. Our results also show that the superiority of the lower-level or the higher-level features over the other ones was determined by the architecture of deep CNNs.
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Affiliation(s)
- Yue Du
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA
| | - Roy Zhang
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
| | - Abolfazl Zargari
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA
| | - Theresa C Thai
- Department of Radiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
| | - Camille C Gunderson
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
| | - Katherine M Moxley
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
| | - Hong Liu
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA
| | - Yuchen Qiu
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.
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Liu L, Wu J, Li D, Senhadji L, Shu H. Fractional Wavelet Scattering Network and Applications. IEEE Trans Biomed Eng 2018; 66:553-563. [PMID: 29993504 DOI: 10.1109/tbme.2018.2850356] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE This study introduces a fractional wavelet scattering network (FrScatNet), which is a generalized translation invariant version of the classical wavelet scattering network. METHODS In our approach, the FrScatNet is constructed based on the fractional wavelet transform (FRWT). The fractional scattering coefficients are iteratively computed using FRWTs and modulus operators. The feature vectors constructed by fractional scattering coefficients are usually used for signal classification. In this paper, an application example of the FrScatNet is provided in order to assess its performance on pathological images. First, the FrScatNet extracts feature vectors from patches of the original histological images under different orders. Then we classify those patches into target (benign or malignant) and background groups. And the FrScatNet property is analyzed by comparing error rates computed from different fractional orders, respectively. Based on the above pathological image classification, a gland segmentation algorithm is proposed by combining the boundary information and the gland location. RESULTS The error rates for different fractional orders of FrScatNet are examined and show that the classification accuracy is improved in fractional scattering domain. We also compare the FrScatNet-based gland segmentation method with those proposed in the 2015 MICCAI Gland Segmentation Challenge and our method achieves comparable results. CONCLUSION The FrScatNet is shown to achieve accurate and robust results. More stable and discriminative fractional scattering coefficients are obtained by the FrScatNet in this paper. SIGNIFICANCE The added fractional order parameter is able to analyze the image in the fractional scattering domain.
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