HistoSSL: Self-Supervised Representation Learning for Classifying Histopathology Images

Xu Jin, Teng Huang (), Ke Wen, Mengxian Chi and Hong An ()
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Xu Jin: School of Computer Science and Technology, University of Science and Technology of China, Hefei 230000, China
Teng Huang: Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou 510000, China
Ke Wen: School of Computer Science and Technology, University of Science and Technology of China, Hefei 230000, China
Mengxian Chi: School of Computer Science and Technology, University of Science and Technology of China, Hefei 230000, China
Hong An: School of Computer Science and Technology, University of Science and Technology of China, Hefei 230000, China

Mathematics, 2022, vol. 11, issue 1, 1-19

Abstract: The success of image classification depends on copious annotated images for training. Annotating histopathology images is costly and laborious. Although several successful self-supervised representation learning approaches have been introduced, they are still insufficient to consider the unique characteristics of histopathology images. In this work, we propose the novel histopathology-oriented self-supervised representation learning framework (HistoSSL) to efficiently extract representations from unlabeled histopathology images at three levels: global, cell, and stain. The model transfers remarkably to downstream tasks: colorectal tissue phenotyping on the NCTCRC dataset and breast cancer metastasis recognition on the CAMELYON16 dataset. HistoSSL achieved higher accuracies than state-of-the-art self-supervised learning approaches, which proved the robustness of the learned representations.

Keywords: digital pathology; self-supervised learning; histopathology image classification; contrastive learning; knowledge distillation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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