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Proceedings Paper

Semi-supervised breast cancer histology classification using deep multiple instance learning and contrast predictive coding (Conference Presentation)

Paper 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.

Paper Details

Date Published: 17 March 2020
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200J (17 March 2020); doi: 10.1117/12.2549627
Show Author Affiliations
Ming Y. Lu, Harvard Medical School (United States)
Richard J. Chen, Harvard Medical School (United States)
Faisal Mahmood, Harvard Medical School (United States)

Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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