Weakly supervised histopathology cancer image segmentation and classification |
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| Affiliation: | 1. Department of Radiology, Stanford University School of Medicine, CA, USA;2. Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland;3. Department of Medicine (Stanford Biomedical Informatics Research), Stanford University School of Medicine, CA, USA;1. Department of Computing Science, Medical Image Analysis Lab, Simon Fraser University, Burnaby, Canada;2. Departments of Pathology and Laboratory Medicine and Obstetrics and Gynaecology, University of British Columbia, Vancouver, Canada;1. School of Electronics Information and Communications, Huazhong University of Science and Technology, Wuhan, PR China;2. CIS Dept., Temple University, Philadelphia, PA 19122, USA;3. University Health Network Canada;4. Tencent AI Lab, PR China;5. Department of CSE, University of Texas at Arlington, Arlington, US;1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China;2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, China;3. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China;4. Medical School, Nanjing University, Nanjing, China;5. National Institute of Healthcare Data Science at Nanjing University, Nanjing, China;6. Department of Medical Imaging, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China;7. Department of Health Administration and Policy, George Mason University, Fairfax, VA, 22030, USA;8. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China;9. Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea |
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| Abstract: | Labeling a histopathology image as having cancerous regions or not is a critical task in cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster them into various classes. Existing supervised approaches for image classification and segmentation require detailed manual annotations for the cancer pixels, which are time-consuming to obtain. In this paper, we propose a new learning method, multiple clustered instance learning (MCIL) (along the line of weakly supervised learning) for histopathology image segmentation. The proposed MCIL method simultaneously performs image-level classification (cancer vs. non-cancer image), medical image segmentation (cancer vs. non-cancer tissue), and patch-level clustering (different classes). We embed the clustering concept into the multiple instance learning (MIL) setting and derive a principled solution to performing the above three tasks in an integrated framework. In addition, we introduce contextual constraints as a prior for MCIL, which further reduces the ambiguity in MIL. Experimental results on histopathology colon cancer images and cytology images demonstrate the great advantage of MCIL over the competing methods. |
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| Keywords: | Image segmentation Classification Clustering Multiple instance learning Histopathology image |
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