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17 March 2020 Semi-supervised breast cancer histology classification using deep multiple instance learning and contrast predictive coding (Conference Presentation)
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Abstract
Subjective interpretation of histology slides forms the basis of cancer diagnosis, prognosis, and therapeutic response prediction. Deep learning models can potentially help serve as an efficient, unbiased tool for this task if trained on large amounts of labeled data. However, labeled medical data, such as small regions of interests, are often costly to curate. In this work, we propose a flexible, semi-supervised framework for histopathological classification that first uses Contrastive Predictive Coding (CPC) to learn semantic features in an unsupervised manner and then use an attention-based multiple Instance Learning (MIL) for classification without requiring patch-level annotations.
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Ming Y. Lu, Richard J. Chen, and Faisal Mahmood "Semi-supervised breast cancer histology classification using deep multiple instance learning and contrast predictive coding (Conference Presentation)", Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200J (17 March 2020); https://doi.org/10.1117/12.2549627
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Cited by 5 scholarly publications.
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