Stain-adaptive self-supervised learning for histopathology image analysis

Staining variability is a critical factor affecting the accuracy of histopathological image analysis by reducing the distinguishability of tissue regions. Existing methods employ preprocessing techniques such as color matching and stain transfer for stain normalization, which can compromise data features. We propose a novel Stain-Adaptive Self-Supervised Learning (SASSL) method for histopathological image analysis. Our SASSL integrates a stain domain adversarial training module into the self-supervised learning (SSL) framework, allowing adaptation to staining variations while learning invariant features. SASSL can be viewed as a general invariant representation SSL method, with derived self-supervised weights applicable to various downstream tasks (classification, regression, and segmentation) in histopathological images. We conducted experiments on publicly available histopathological image analysis datasets, including PANDA, BreastPathQ, and CAMELYON16, achieving state-of-the-art performance. Results demonstrate that SASSL enhances feature extraction and mitigates the impact of staining variability, consistently improving performance across tasks. Our code is available at https://github.com/YeahHighly/SASSL_PR_2024.