Weakly supervised histopathological image representation learning based on contrastive dynamic clustering

Jun Li; Zhiguo Jiang; Yushan Zheng; Haopeng Zhang; Jun Shi; Dingyi Hu; Wei Luo; Zhongmin Jiang; Chenghai Xue

doi:10.1117/12.2611418

4 April 2022 Weakly supervised histopathological image representation learning based on contrastive dynamic clustering

Jun Li, Zhiguo Jiang, Yushan Zheng, Haopeng Zhang, Jun Shi, Dingyi Hu, Wei Luo, Zhongmin Jiang, Chenghai Xue

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 1203905 (2022) https://doi.org/10.1117/12.2611418
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Feature representations of histopathology whole slide images (WSIs) are crucial to the downstream applications for computer-aided cancer diagnosis, including whole slide image classification, region of interest detection, hash retrieval, prognosis analysis, and other high-level inference tasks. State-of-the-art methods for whole slide image feature extraction generally rely on supervised learning algorithms based on fine-grained manual annotations, unsupervised learning algorithms without annotation, or directly use pre-trained features. At present, there is a lack of research on weakly supervised feature learning methods that only utilize WSI-level labeling. In this paper, we propose a weakly supervised framework that learns the feature representations of various lesion areas from histopathology whole slide images. The proposed framework consists of a contrastive learning network as the backbone and a designed contrastive dynamic clustering (CDC) module to embedding the lesion information into the feature representations. The proposed method was evaluated on a large scale endometrial whole slide image dataset. The experimental results have demonstrated that our method can learn discriminative feature representations for histopathology image classification and the quantitative performance of our method is close to the fully-supervision learning methods. The code is available at https://github.com/junl21/cdc.

Conference Presentation

Citation Download Citation

Jun Li, Zhiguo Jiang, Yushan Zheng, Haopeng Zhang, Jun Shi, Dingyi Hu, Wei Luo, Zhongmin Jiang, and Chenghai Xue "Weakly supervised histopathological image representation learning based on contrastive dynamic clustering", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 1203905 (4 April 2022); https://doi.org/10.1117/12.2611418

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