Contrastive Learning for Histopathological Image Analysis

Histopathology is a demanding setting for machine learning because the most important signals are often highly localized, while the available labels are typically sparse, coarse, or expensive to obtain. Whole-slide images contain enormous visual detail, but detailed patch-level annotation requires substantial expert effort, making it difficult to learn robust local representations at scale. Our work in histopathology contrastive learning is motivated by this mismatch. We aim to build representations that are useful for both slide-level diagnosis and fine-grained region understanding, improving performance in settings where local tissue patterns matter clinically and where standard pretrained features may not adequately capture the structure of pathology imagery.

Methodologically, we develop contrastive learning strategies that are tailored to pathology rather than borrowed unchanged from natural-image pipelines. One direction leverages the distinct but complementary information carried by hematoxylin and eosin, using adaptive stain separation to form meaningful paired views and combining contrastive training with pseudo-labeling and MixUp to better use limited labeled data (see Figure below). A second direction addresses weakly supervised whole-slide learning by incorporating slide-level labels directly into representation learning: features from negative slides are encouraged to cluster, while patches from positive slides are trained with a contrastive objective that preserves diversity without assuming unreliable patch labels. Together, these methods seek to produce more discriminative and pathology-aware embeddings that support stronger downstream classification and multiple-instance learning.