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Saikia S, Si T, Deb D, Bora K, Mallik S, Maulik U, Zhao Z. Lesion detection in women breast's dynamic contrast-enhanced magnetic resonance imaging using deep learning. Sci Rep 2023; 13:22555. [PMID: 38110462 PMCID: PMC10728155 DOI: 10.1038/s41598-023-48553-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 11/28/2023] [Indexed: 12/20/2023] Open
Abstract
Breast cancer is one of the most common cancers in women and the second foremost cause of cancer death in women after lung cancer. Recent technological advances in breast cancer treatment offer hope to millions of women in the world. Segmentation of the breast's Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is one of the necessary tasks in the diagnosis and detection of breast cancer. Currently, a popular deep learning model, U-Net is extensively used in biomedical image segmentation. This article aims to advance the state of the art and conduct a more in-depth analysis with a focus on the use of various U-Net models in lesion detection in women's breast DCE-MRI. In this article, we perform an empirical study of the effectiveness and efficiency of U-Net and its derived deep learning models including ResUNet, Dense UNet, DUNet, Attention U-Net, UNet++, MultiResUNet, RAUNet, Inception U-Net and U-Net GAN for lesion detection in breast DCE-MRI. All the models are applied to the benchmarked 100 Sagittal T2-Weighted fat-suppressed DCE-MRI slices of 20 patients and their performance is compared. Also, a comparative study has been conducted with V-Net, W-Net, and DeepLabV3+. Non-parametric statistical test Wilcoxon Signed Rank Test is used to analyze the significance of the quantitative results. Furthermore, Multi-Criteria Decision Analysis (MCDA) is used to evaluate overall performance focused on accuracy, precision, sensitivity, F[Formula: see text]-score, specificity, Geometric-Mean, DSC, and false-positive rate. The RAUNet segmentation model achieved a high accuracy of 99.76%, sensitivity of 85.04%, precision of 90.21%, and Dice Similarity Coefficient (DSC) of 85.04% whereas ResNet achieved 99.62% accuracy, 62.26% sensitivity, 99.56% precision, and 72.86% DSC. ResUNet is found to be the most effective model based on MCDA. On the other hand, U-Net GAN takes the least computational time to perform the segmentation task. Both quantitative and qualitative results demonstrate that the ResNet model performs better than other models in segmenting the images and lesion detection, though computational time in achieving the objectives varies.
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Affiliation(s)
- Sudarshan Saikia
- Information Technology Department, Oil India Limited, Duliajan, Assam, 786602, India
| | - Tapas Si
- AI Innovation Lab, Department of Computer Science & Engineering, University of Engineering & Management, Jaipur, GURUKUL, Jaipur, Rajasthan, 303807, India
| | - Darpan Deb
- Department of Computer Application, Christ University, Bengaluru, 560029, India
| | - Kangkana Bora
- Department of Computer Science and Information Technology, Cotton University, Guwahati, Assam, 781001, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Ujjwal Maulik
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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Farooq MU, Ullah Z, Gwak J. Residual attention based uncertainty-guided mean teacher model for semi-supervised breast masses segmentation in 2D ultrasonography. Comput Med Imaging Graph 2023; 104:102173. [PMID: 36641970 DOI: 10.1016/j.compmedimag.2022.102173] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 10/12/2022] [Accepted: 12/27/2022] [Indexed: 01/11/2023]
Abstract
Breast tumor is the second deadliest disease among women around the world. Earlier tumor diagnosis is extremely important for improving the survival rate. Recent deep-learning techniques proved helpful in the timely diagnosis of various tumors. However, in the case of breast tumors, the characteristics of the tumors, i.e., low visual contrast, unclear boundary, and diversity in shape and size of breast lesions, make it more challenging to design a highly efficient detection system. Additionally, the scarcity of publicly available labeled data is also a major hurdle in the development of highly accurate and robust deep-learning models for breast tumor detection. To overcome these issues, we propose residual-attention-based uncertainty-guided mean teacher framework which incorporates the residual and attention blocks. The residual for optimizing the deep network by enabling the flow of high-level features and attention modules improves the focus of the model by optimizing its weights during the learning process. We further explore the potential of utilizing unlabeled data during the training process by employing the semi-supervised learning (SSL) method. Particularly, the uncertainty-guided mean-teacher student architecture is exploited to demonstrate the potential of incorporating the unlabeled samples during the training of residual attention U-Net model. The proposed SSL framework has been rigorously evaluated on two publicly available labeled datasets, i.e., BUSI and UDIAT datasets. The quantitative as well as qualitative results demonstrate that the proposed framework achieved competitive performance with respect to the previous state-of-the-art techniques and outperform the existing breast ultrasound masses segmentation techniques. Most importantly, the study demonstrates the potential of incorporating the additional unlabeled data for improving the performance of breast tumor segmentation.
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Affiliation(s)
- Muhammad Umar Farooq
- Department of IT Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, South Korea.
| | - Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju 27469, South Korea.
| | - Jeonghwan Gwak
- Department of IT Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, South Korea; Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, South Korea; Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, South Korea.
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WANG MONAN, LI DONGHUI, TANG LI. A LUNG IMAGE CLASSIFICATION METHOD: A CLASSIFIER CONSTRUCTED BY COMBINING IMPROVED VGG16 AND GRADIENT BOOSTING DECISION TREE. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421500421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Early classification and diagnosis of lung diseases is essential to increase the best chance of patient recovery and survival. Using deep learning to make it possible, the key is how to improve the robustness of the deep learning model and the accuracy of lung image classification. In order to classify the five lung diseases, we used transfer learning to improve and fine-tune the fully connected layer of VGG16, and improve the cross entropy loss function, combined with the gradient boosting decision tree (GBDT), to establish a deep learning model called a classifier. The model was trained using the ChestX-ray14 dataset. On the test set, the classification accuracy of our model for the five lung diseases was 82.43%, 95.37%, 82.11%, 79.81%, 78.13%, which is better than the best published results. The F1 value is 0.456 (95% CI 0.415, 0.496). The robustness of the model exceeds CheXNet and average performance of doctors. This study clarified that the model has strong robustness and effectiveness in classifying five lung diseases.
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Affiliation(s)
- MONAN WANG
- Harbin University of Science and Technology, Harbin 150080, P. R. China
| | - DONGHUI LI
- Harbin University of Science and Technology, Harbin 150080, P. R. China
| | - LI TANG
- Harbin University of Science and Technology, Harbin 150080, P. R. China
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Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S. Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model. Sci Rep 2017; 7:4172. [PMID: 28646155 PMCID: PMC5482871 DOI: 10.1038/s41598-017-04075-z] [Citation(s) in RCA: 165] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 05/09/2017] [Indexed: 11/12/2022] Open
Abstract
Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.
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Affiliation(s)
- Zhongyi Han
- College of Science and Technology, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Benzheng Wei
- College of Science and Technology, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
- Institute of evidence based Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, China
| | - Yilong Yin
- School of Computer Science and Technology, Shandong University, Jinan, 250100, China
| | - Kejian Li
- Institute of evidence based Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Shuo Li
- Department of Medical Imaging, Western University, London, N6A 4V2, Canada
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Zheng Y, Wang Y, Jiao W, Hou S, Ren Y, Qin M, Hou D, Luo C, Wang H, Gee J, Zhao B. Joint alignment of multispectral images via semidefinite programming. BIOMEDICAL OPTICS EXPRESS 2017; 8:890-901. [PMID: 28270991 PMCID: PMC5330559 DOI: 10.1364/boe.8.000890] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Revised: 01/08/2017] [Accepted: 01/09/2017] [Indexed: 06/06/2023]
Abstract
In this paper, we introduce a novel feature-point-matching based framework for achieving an optimized joint-alignment of sequential images from multispectral imaging (MSI). It solves a low-rank and semidefinite matrix that stores all pairwise-image feature-mappings by minimizing the total amount of point-to-point matching cost via a convex optimization of a semidefinite programming formulation. This unique strategy takes a complete consideration of the information aggregated by all point-matching costs and enables the entire set of pairwise-image feature-mappings to be solved simultaneously and near-optimally. Our framework is capable of running in an automatic or interactive fashion, offering an effective tool for eliminating spatial misalignments introduced into sequential MSI images during the imaging process. Our experimental results obtained from a database of 28 sequences of MSI images of human eye demonstrate the superior performances of our approach to the state-of-the-art techniques. Our framework is potentially invaluable in a large variety of practical applications of MSI images.
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Affiliation(s)
- Yuanjie Zheng
- School of Information Science & Engineering, Shandong Normal University, Jinan,
China
- Institute of Life Sciences at Shandong Normal University, Jinan,
China
- Key Lab of Intelligent Information Processing at Shandong Normal University, Jinan,
China
| | - Yu Wang
- School of Information Science & Engineering, Shandong Normal University, Jinan,
China
| | - Wanzhen Jiao
- Dept. of Ophthalmology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan,
China
| | - Sujuan Hou
- School of Information Science & Engineering, Shandong Normal University, Jinan,
China
| | - Yanju Ren
- School of Psychology, Shandong Normal University, Jinan,
China
| | - Maoling Qin
- School of Information Science & Engineering, Shandong Normal University, Jinan,
China
| | - Dewen Hou
- School of Information Science & Engineering, Shandong Normal University, Jinan,
China
| | - Chao Luo
- School of Information Science & Engineering, Shandong Normal University, Jinan,
China
| | - Hong Wang
- School of Information Science & Engineering, Shandong Normal University, Jinan,
China
- Institute of Life Sciences at Shandong Normal University, Jinan,
China
- Key Lab of Intelligent Information Processing at Shandong Normal University, Jinan,
China
| | - James Gee
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,
USA
| | - Bojun Zhao
- Dept. of Ophthalmology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan,
China
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