Stain-Adaptive Self-Supervised Learning for Histopathology Image Analysis
This addresses stain variation issues in histopathology image analysis, which is critical for medical diagnosis, but it appears incremental as it builds on existing self-supervised and domain adaptation techniques.
The paper tackles the problem of color variations in histopathology images by proposing a stain-adaptive self-supervised learning method that integrates domain-adversarial training to learn robust features, achieving state-of-the-art performance on datasets like PANDA, BreastPathQ, and CAMELYON16.
It is commonly recognized that color variations caused by differences in stains is a critical issue for histopathology image analysis. Existing methods adopt color matching, stain separation, stain transfer or the combination of them to alleviate the stain variation problem. In this paper, we propose a novel Stain-Adaptive Self-Supervised Learning(SASSL) method for histopathology image analysis. Our SASSL integrates a domain-adversarial training module into the SSL framework to learn distinctive features that are robust to both various transformations and stain variations. The proposed SASSL is regarded as a general method for domain-invariant feature extraction which can be flexibly combined with arbitrary downstream histopathology image analysis modules (e.g. nuclei/tissue segmentation) by fine-tuning the features for specific downstream tasks. We conducted experiments on publicly available pathological image analysis datasets including the PANDA, BreastPathQ, and CAMELYON16 datasets, achieving the state-of-the-art performance. Experimental results demonstrate that the proposed method can robustly improve the feature extraction ability of the model, and achieve stable performance improvement in downstream tasks.