PMIL: A Topology Module to Improve MIL-based WSI Classification

Ahmad Obeid, Anabia Sohail, Said Boumaraf, Xiabi Liu, Sajid Javed, Hasan Almarzouqi, Jorge Dias, Mohammed Bennamoun, Naoufel Werghi, Ibrahim Abe M. Elfadel

Abstract: Deep learning models have achieved remarkable success in pathology image analysis. However, they still face challenges in effectively modeling fine-grained, object-level features. Topological Data Analysis (TDA) has shown promise for addressing these issues but remains underexplored, particularly for whole-slide pathology applications. Additionally, the effectiveness of TDA has yet to be firmly established, as current studies largely use small-scale datasets. In this work, we address these gaps by introducing Persistent Homology in Multiple Instance Learning (PMIL), the first adaptable TDA-based module within the MIL framework. We validate our approach on a large-scale classification dataset, benchmarking against multiple state-of-the-art methods.