TopoSlide: Topologically-Informed Histopathology Whole Slide Image Representation Learning

Histopathology whole slide images are massive gigapixel images that present significant challenges in generating effective representations that accurately capture their histological content and the spatial organization of their various components. In this study, we introduce TopoSlide, a novel approach for self-supervised representation learning specifically designed for whole slide histopathology images. Our method leverages topological features of image data to optimize the learning process. We demonstrate that TopoSlide, even when trained on relatively small datasets, achieves comparable or superior performance to existing pathology foundation models across multiple retrieval and linear probing benchmarks.