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Using Deep Learning with Bayesian–Gaussian Inspired Convolutional Neural Architectural Search for Cancer Recognition and Classification from Histopathological Image Frames. JOURNAL OF HEALTHCARE ENGINEERING 2023. [DOI: 10.1155/2023/4597445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
We propose a neural architectural search model which examines histopathological images to detect the presence of cancer in both lung and colon tissues. In recent times, deep artificial neural networks have made tremendous impacts in healthcare. However, obtaining an optimal artificial neural network model that could yield excellent performance during training, evaluation, and inferencing has been a bottleneck for researchers. Our method uses a Bayesian convolutional neural architectural search algorithm in collaboration with Gaussian processes to provide an efficient neural network architecture for efficient colon and lung cancer classification and recognition. The proposed model learns by using the Gaussian process to estimate the required optimal architectural values by choosing a set of model parameters through the exploitation of the expected improvement (EI) values, thereby minimizing the number of sampled trials and suggesting the best model architecture. Several experiments were conducted, and a landmark performance was obtained in both validation and test data through the evaluation of the proposed model on a dataset consisting of 25,000 images of five different classes with convergence and F1-score matrices.
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Sethy PK, Geetha Devi A, Padhan B, Behera SK, Sreedhar S, Das K. Lung cancer histopathological image classification using wavelets and AlexNet. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:211-221. [PMID: 36463485 DOI: 10.3233/xst-221301] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Among malignant tumors, lung cancer has the highest morbidity and fatality rates worldwide. Screening for lung cancer has been investigated for decades in order to reduce mortality rates of lung cancer patients, and treatment options have improved dramatically in recent years. Pathologists utilize various techniques to determine the stage, type, and subtype of lung cancers, but one of the most common is a visual assessment of histopathology slides. The most common subtypes of lung cancer are adenocarcinoma and squamous cell carcinoma, lung benign, and distinguishing between them requires visual inspection by a skilled pathologist. The purpose of this article was to develop a hybrid network for the categorization of lung histopathology images, and it did so by combining AlexNet, wavelet, and support vector machines. In this study, we feed the integrated discrete wavelet transform (DWT) coefficients and AlexNet deep features into linear support vector machines (SVMs) for lung nodule sample classification. The LC25000 Lung and colon histopathology image dataset, which contains 5,000 digital histopathology images in three categories of benign (normal cells), adenocarcinoma, and squamous carcinoma cells (both are cancerous cells) is used in this study to train and test SVM classifiers. The study results of using a 10-fold cross-validation method achieve an accuracy of 99.3% and an area under the curve (AUC) of 0.99 in classifying these digital histopathology images of lung nodule samples.
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Affiliation(s)
| | - A Geetha Devi
- Department of Electronics and Communication Engineering, PVP Siddhartha Institute of Technology, Vijayawada, AP, India
| | - Bikash Padhan
- Department of Electronics, Sambalpur University, Jyoti Vihar, Burla, India
| | | | | | - Kalyan Das
- Department Computer Science Engineering and Application, Sambalpur University Institute of Information Technology, Burla, India
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He W, Liu T, Han Y, Ming W, Du J, Liu Y, Yang Y, Wang L, Jiang Z, Wang Y, Yuan J, Cao C. A review: The detection of cancer cells in histopathology based on machine vision. Comput Biol Med 2022; 146:105636. [PMID: 35751182 DOI: 10.1016/j.compbiomed.2022.105636] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/04/2022] [Accepted: 04/28/2022] [Indexed: 12/24/2022]
Abstract
Machine vision is being employed in defect detection, size measurement, pattern recognition, image fusion, target tracking and 3D reconstruction. Traditional cancer detection methods are dominated by manual detection, which wastes time and manpower, and heavily relies on the pathologists' skill and work experience. Therefore, these manual detection approaches are not convenient for the inheritance of domain knowledge, and are not suitable for the rapid development of medical care in the future. The emergence of machine vision can iteratively update and learn the domain knowledge of cancer cell pathology detection to achieve automated, high-precision, and consistent detection. Consequently, this paper reviews the use of machine vision to detect cancer cells in histopathology images, as well as the benefits and drawbacks of various detection approaches. First, we review the application of image preprocessing and image segmentation in histopathology for the detection of cancer cells, and compare the benefits and drawbacks of different algorithms. Secondly, for the characteristics of histopathological cancer cell images, the research progress of shape, color and texture features and other methods is mainly reviewed. Furthermore, for the classification methods of histopathological cancer cell images, the benefits and drawbacks of traditional machine vision approaches and deep learning methods are compared and analyzed. Finally, the above research is discussed and forecasted, with the expected future development tendency serving as a guide for future research.
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Affiliation(s)
- Wenbin He
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Ting Liu
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yongjie Han
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Wuyi Ming
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China; Guangdong HUST Industrial Technology Research Institute, Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Dongguan, 523808, China.
| | - Jinguang Du
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yinxia Liu
- Laboratory Medicine of Dongguan Kanghua Hospital, Dongguan, 523808, China
| | - Yuan Yang
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, China.
| | - Leijie Wang
- School of Mechanical Engineering, Dongguan University of Technology Dongguan, 523808, China
| | - Zhiwen Jiang
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yongqiang Wang
- Zhengzhou Coal Mining Machinery Group Co., Ltd, Zhengzhou, 450016, China
| | - Jie Yuan
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Chen Cao
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China; Guangdong HUST Industrial Technology Research Institute, Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Dongguan, 523808, China
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Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5269913. [PMID: 36704098 PMCID: PMC9873459 DOI: 10.1155/2022/5269913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 06/22/2022] [Accepted: 07/14/2022] [Indexed: 01/31/2023]
Abstract
Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging. Accurate categorization of colon cancers is necessary for capable analysis. Our objective is to promote a system for detecting and classifying colon adenocarcinomas by applying a deep convolutional neural network (DCNN) model with some preprocessing techniques on digital histopathology images. It is a leading cause of cancer-related death, despite the fact that both traditional and modern methods are capable of comparing images that may encompass cancer regions of various sorts after looking at a significant number of colon cancer images. The fundamental problem for colon histopathologists is differentiating benign from malignant illnesses to having some complicated factors. A cancer diagnosis can be automated through artificial intelligence (AI), enabling us to appraise more patients in less time and at a decreased cost. Modern deep learning (MDL) and digital image processing (DIP) approaches are used to accomplish this. The results indicate that the proposed structure can accurately analyze cancer tissues to a maximum of 99.80%. By implementing this approach, medical practitioners will establish an automated and reliable system for detecting various forms of colon cancer. Moreover, CAD systems will be built in the near future to extract numerous aspects from colonoscopic images for use as a preprocessing module for colon cancer diagnosis.
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Masud M, Sikder N, Nahid AA, Bairagi AK, AlZain MA. A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework. SENSORS 2021; 21:s21030748. [PMID: 33499364 PMCID: PMC7865416 DOI: 10.3390/s21030748] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 01/10/2021] [Accepted: 01/18/2021] [Indexed: 12/19/2022]
Abstract
The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.
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Affiliation(s)
- Mehedi Masud
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
- Correspondence:
| | - Niloy Sikder
- Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh; (N.S.); (A.K.B.)
| | - Abdullah-Al Nahid
- Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh;
| | - Anupam Kumar Bairagi
- Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh; (N.S.); (A.K.B.)
| | - Mohammed A. AlZain
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
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Deng S, Zhang X, Yan W, Chang EIC, Fan Y, Lai M, Xu Y. Deep learning in digital pathology image analysis: a survey. Front Med 2020; 14:470-487. [PMID: 32728875 DOI: 10.1007/s11684-020-0782-9] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 03/05/2020] [Indexed: 12/21/2022]
Abstract
Deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. In terms of feature extraction, DL approaches are less labor intensive compared with conventional machine learning methods. In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classification, semantic segmentation, detection, and instance segmentation) and various applications (e.g., stain normalization, cell/gland/region structure analysis). DL methods can provide consistent and accurate outcomes. DL is a promising tool to assist pathologists in clinical diagnosis.
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Affiliation(s)
- Shujian Deng
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
- Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China
| | - Xin Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
- Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China
| | - Wen Yan
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
- Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China
| | | | - Yubo Fan
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
- Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China
| | - Maode Lai
- Department of Pathology, School of Medicine, Zhejiang University, Hangzhou, 310007, China
| | - Yan Xu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
- Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China.
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China.
- Microsoft Research Asia, Beijing, 100080, China.
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Guo R, Yang SH, Lu F, Han ZH, Yan X, Fu CX, Zhao ML, Lin J. Evaluation of intratumoral heterogeneity by using diffusion kurtosis imaging and stretched exponential diffusion-weighted imaging in an orthotopic hepatocellular carcinoma xenograft model. Quant Imaging Med Surg 2019; 9:1566-1578. [PMID: 31667142 DOI: 10.21037/qims.2019.08.18] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background To investigate the value of diffusion kurtosis imaging (DKI) and diffusion-weighted imaging (DWI) with a stretched exponential model (SEM) in the evaluation of tumor heterogeneity in an orthotopic hepatocellular carcinoma (HCC) xenograft model. Methods Thirty orthotopic HCC xenograft nude mice models were established and randomly divided into two groups, the sorafenib induction group (n=15) and control group (n=15). Every mouse in each group underwent MRI with DKI and SEM on a 1.5T MR scanner at 7, 14, and 21 days after sorafenib intervention. DKI and SEM parameters including mean kurtosis (MK), mean diffusivity (MD), α, and distributed diffusion coefficient (DDC) were measured, calculated, and compared between the two groups and among different time points. Sequential correlations between histopathological results including necrotic fraction (NF), micro-vessel density (MVD), Ki-67 index, standard deviation (SD), and kurtosis from hematoxylin-eosin staining, and DKI and SEM parameters were analyzed. Results MK, MD, and DDC of HCC in the sorafenib induction group were significantly higher than those in the control group at each time point (P<0.05), while α was significantly lower (P<0.05). Significantly positive correlations were found between MK and NF (r=0.693, P=0.010), SD (r =0.785, P=0.003), kurtosis (r=0.779, P=0.003), between MD and NF (r=0.794, P=0.003), SD (r=0.629, P=0.020), kurtosis (r=0.645, P=0.018), and between DDC and NF (r=0.800, P=0.003), SD (r=0.636, P=0.020), kurtosis (r=0.664, P=0.016), and significantly negative correlations were observed between α and NF (r=-0.704, P=0.009), SD (r=-0.754, P=0.003), and kurtosis (r=-0.792, P=0.003) in the sorafenib induction group. Conclusions DKI and SEM parameters may be potentially useful for evaluating intratumoral heterogeneity in HCC.
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Affiliation(s)
- Ran Guo
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Shuo-Hui Yang
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200021, China
| | - Fang Lu
- Department of Radiology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200021, China
| | - Zhi-Hong Han
- Department of Pathology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200021, China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthcare, Shanghai 201318, China
| | - Cai-Xia Fu
- Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen 518057, China
| | - Meng-Long Zhao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Jiang Lin
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,Shanghai Institute of Medical Imaging, Shanghai 200032, China
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Chaddad A, Daniel P, Niazi T. Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images. Front Oncol 2018; 8:96. [PMID: 29670857 PMCID: PMC5893871 DOI: 10.3389/fonc.2018.00096] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 03/19/2018] [Indexed: 12/18/2022] Open
Abstract
Purpose Colorectal cancer (CRC) is markedly heterogeneous and develops progressively toward malignancy through several stages which include stroma (ST), benign hyperplasia (BH), intraepithelial neoplasia (IN) or precursor cancerous lesion, and carcinoma (CA). Identification of the malignancy stage of CRC pathology tissues (PT) allows the most appropriate therapeutic intervention. Methods This study investigates multiscale texture features extracted from CRC pathology sections using 3D wavelet transform (3D-WT) filter. Multiscale features were extracted from digital whole slide images of 39 patients that were segmented in a pre-processing step using an active contour model. The capacity for multiscale texture to compare and classify between PTs was investigated using ANOVA significance test and random forest classifier models, respectively. Results 12 significant features derived from the multiscale texture (i.e., variance, entropy, and energy) were found to discriminate between CRC grades at a significance value of p < 0.01 after correction. Combining multiscale texture features lead to a better predictive capacity compared to prediction models based on individual scale features with an average (±SD) classification accuracy of 93.33 (±3.52)%, sensitivity of 88.33 (± 4.12)%, and specificity of 96.89 (± 3.88)%. Entropy was found to be the best classifier feature across all the PT grades with an average of the area under the curve (AUC) value of 91.17, 94.21, 97.70, 100% for ST, BH, IN, and CA, respectively. Conclusion Our results suggest that multiscale texture features based on 3D-WT are sensitive enough to discriminate between CRC grades with the entropy feature, the best predictor of pathology grade.
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Affiliation(s)
- Ahmad Chaddad
- Division of Radiation Oncology, McGill University, Montreal, QC, Canada
| | - Paul Daniel
- Division of Radiation Oncology, McGill University, Montreal, QC, Canada
| | - Tamim Niazi
- Division of Radiation Oncology, McGill University, Montreal, QC, Canada
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Mercan C, Aksoy S, Mercan E, Shapiro LG, Weaver DL, Elmore JG. Multi-Instance Multi-Label Learning for Multi-Class Classification of Whole Slide Breast Histopathology Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:316-325. [PMID: 28981408 PMCID: PMC5774338 DOI: 10.1109/tmi.2017.2758580] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Digital pathology has entered a new era with the availability of whole slide scanners that create the high-resolution images of full biopsy slides. Consequently, the uncertainty regarding the correspondence between the image areas and the diagnostic labels assigned by pathologists at the slide level, and the need for identifying regions that belong to multiple classes with different clinical significances have emerged as two new challenges. However, generalizability of the state-of-the-art algorithms, whose accuracies were reported on carefully selected regions of interest (ROIs) for the binary benign versus cancer classification, to these multi-class learning and localization problems is currently unknown. This paper presents our potential solutions to these challenges by exploiting the viewing records of pathologists and their slide-level annotations in weakly supervised learning scenarios. First, we extract candidate ROIs from the logs of pathologists' image screenings based on different behaviors, such as zooming, panning, and fixation. Then, we model each slide with a bag of instances represented by the candidate ROIs and a set of class labels extracted from the pathology forms. Finally, we use four different multi-instance multi-label learning algorithms for both slide-level and ROI-level predictions of diagnostic categories in whole slide breast histopathology images. Slide-level evaluation using 5-class and 14-class settings showed average precision values up to 81% and 69%, respectively, under different weakly labeled learning scenarios. ROI-level predictions showed that the classifier could successfully perform multi-class localization and classification within whole slide images that were selected to include the full range of challenging diagnostic categories.
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Wang P, Ge R, Xiao X, Zhou M, Zhou F. hMuLab: A Biomedical Hybrid MUlti-LABel Classifier Based on Multiple Linear Regression. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:1173-1180. [PMID: 28113599 DOI: 10.1109/tcbb.2016.2603507] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Many biomedical classification problems are multi-label by nature, e.g., a gene involved in a variety of functions and a patient with multiple diseases. The majority of existing classification algorithms assumes each sample with only one class label, and the multi-label classification problem remains to be a challenge for biomedical researchers. This study proposes a novel multi-label learning algorithm, hMuLab, by integrating both feature-based and neighbor-based similarity scores. The multiple linear regression modeling techniques make hMuLab capable of producing multiple label assignments for a query sample. The comparison results over six commonly-used multi-label performance measurements suggest that hMuLab performs accurately and stably for the biomedical datasets, and may serve as a complement to the existing literature.
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11
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Texture Analysis of Abnormal Cell Images for Predicting the Continuum of Colorectal Cancer. Anal Cell Pathol (Amst) 2017; 2017:8428102. [PMID: 28331793 PMCID: PMC5282460 DOI: 10.1155/2017/8428102] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 08/20/2015] [Indexed: 11/17/2022] Open
Abstract
Abnormal cell (ABC) is a markedly heterogeneous tissue area and can be categorized into three main types: benign hyperplasia (BH), carcinoma (Ca), and intraepithelial neoplasia (IN) or precursor cancerous lesion. In this study, the goal is to determine and characterize the continuum of colorectal cancer by using a 3D-texture approach. ABC was segmented in preprocessing step using an active contour segmentation technique. Cell types were analyzed based on textural features extracted from the gray level cooccurrence matrices (GLCMs). Significant texture features were selected using an analysis of variance (ANOVA) of ABC with a p value cutoff of p < 0.01. Features selected were reduced with a principal component analysis (PCA), which accounted for 97% of the cumulative variance from significant features. The simulation results identified 158 significant features based on ANOVA from a total of 624 texture features extracted from GLCMs. Performance metrics of ABC discrimination based on significant texture features showed 92.59% classification accuracy, 100% sensitivity, and 94.44% specificity. These findings suggest that texture features extracted from GLCMs are sensitive enough to discriminate between the ABC types and offer the opportunity to predict cell characteristics of colorectal cancer.
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Qiu WR, Zheng QS, Sun BQ, Xiao X. Multi-iPPseEvo: A Multi-label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou′s General PseAAC via Grey System Theory. Mol Inform 2016; 36. [DOI: 10.1002/minf.201600085] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 09/07/2016] [Indexed: 01/19/2023]
Affiliation(s)
- Wang-Ren Qiu
- Computer Department; Jingdezhen Ceramic Institute; Jingdezhen 333403 China
- Department of Computer Science; University of Missouri; Columbia, MO USA
- Bond Life Science Center; University of Missouri; Columbia, MO USA
| | - Quan-Shu Zheng
- Computer Department; Jingdezhen Ceramic Institute; Jingdezhen 333403 China
| | - Bi-Qian Sun
- Computer Department; Jingdezhen Ceramic Institute; Jingdezhen 333403 China
| | - Xuan Xiao
- Computer Department; Jingdezhen Ceramic Institute; Jingdezhen 333403 China
- Gordon Life Science Institute; Boston, Massachusetts 02478 United States of America
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Liu H, Ma Z, Han J, Chen Z, Zheng Z. Regularized partial least squares for multi-label learning. INT J MACH LEARN CYB 2016. [DOI: 10.1007/s13042-016-0500-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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14
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Lan X, Li L, Hu J, Zhang Q, Dang Y, Huang Y. A Quantitative Method for Microtubule Analysis in Fluorescence Images. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2015; 21:1582-1590. [PMID: 26417862 DOI: 10.1017/s1431927615015202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Microtubule analysis is of significant value for a better understanding of normal and pathological cellular processes. Although immunofluorescence microscopic techniques have proven useful in the study of microtubules, comparative results commonly rely on a descriptive and subjective visual analysis. We developed an objective and quantitative method based on image processing and analysis of fluorescently labeled microtubular patterns in cultured cells. We used a multi-parameter approach by analyzing four quantifiable characteristics to compose our quantitative feature set. Then we interpreted specific changes in the parameters and revealed the contribution of each feature set using principal component analysis. In addition, we verified that different treatment groups could be clearly discriminated using principal components of the multi-parameter model. High predictive accuracy of four commonly used multi-classification methods confirmed our method. These results demonstrated the effectiveness and efficiency of our method in the analysis of microtubules in fluorescence images. Application of the analytical methods presented here provides information concerning the organization and modification of microtubules, and could aid in the further understanding of structural and functional aspects of microtubules under normal and pathological conditions.
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Affiliation(s)
- Xiaodong Lan
- State Key Laboratory of Trauma,Burns and Combined Injury,Institute of Burn Research,Southwest Hospital,The Third Military Medical University,Chongqing 400038,China
| | - Lingfei Li
- State Key Laboratory of Trauma,Burns and Combined Injury,Institute of Burn Research,Southwest Hospital,The Third Military Medical University,Chongqing 400038,China
| | - Jiongyu Hu
- State Key Laboratory of Trauma,Burns and Combined Injury,Institute of Burn Research,Southwest Hospital,The Third Military Medical University,Chongqing 400038,China
| | - Qiong Zhang
- State Key Laboratory of Trauma,Burns and Combined Injury,Institute of Burn Research,Southwest Hospital,The Third Military Medical University,Chongqing 400038,China
| | - Yongming Dang
- State Key Laboratory of Trauma,Burns and Combined Injury,Institute of Burn Research,Southwest Hospital,The Third Military Medical University,Chongqing 400038,China
| | - Yuesheng Huang
- State Key Laboratory of Trauma,Burns and Combined Injury,Institute of Burn Research,Southwest Hospital,The Third Military Medical University,Chongqing 400038,China
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