Spatial Context-Driven Positive Pair Sampling for Enhanced Histopathology Image Classification

Deep learning has demonstrated great promise in cancer classification from whole-slide images (WSIs) but remains constrained by the need for extensive annotations. Annotation-free methods, such as multiple instance learning (MIL) and self-supervised learning (SSL), have emerged to address this challenge; however, current SSL techniques often depend on synthetic augmentations or temporal context, which may not adequately capture the intricate spatial relationships inherent to histopathology. In this work, we introduce a novel spatial context-driven positive pair sampling strategy for SSL that leverages the natural coherence of adjacent patches in WSIs. By constructing biologically relevant positive pairs from spatially proximate patches, our approach harnesses inherent spatial coherence to enhance patch-level representations, ultimately boosting slide-level classification performance. Experiments on multiple datasets reveal that our strategy improves classification accuracy by 5\% to 10\% over the standard method, paving the way for more clinically relevant AI models in cancer diagnosis. The code is available at https://anonymous.4open.science/r/contextual-pairs-E72F/.