Enhancing Whole Slide Image Classification with Discriminative and Contrastive Learning

Whole slide image (WSI) classification plays a crucial role in digital pathology data analysis. However, the immense size of WSIs and the absence of fine-grained sub-region labels, such as patches, pose significant challenges for accurate WSI classification. Typical classification-driven deep learning methods often struggle to generate compact image representations, which can compromise the robustness of WSI classification. In this study, we address this challenge by incorporating both discriminative and contrastive learning techniques for WSI classification. Different from the extant contrastive learning methods for WSI classification that primarily assign pseudo labels to patches based on the WSI-level labels, our approach takes a different route to directly focus on constructing positive and negative samples at the WSI-level. Specifically, we select a subset of representative and informative patches to represent WSIs and create positive and negative samples at the WSI-level, allowing us to better capture WSI-level information and increase the likelihood of effectively learning informative features. Experimental results on two datasets and ablation studies have demonstrated that our method significantly improved the WSI classification performance compared to state-of-the-art deep learning methods and enabled learning of informative features that promoted robustness of the WSI classification.