Enhancing Prostate Cancer Subtyping: A Persistent Homology Approach in Multiple Instance Learning
Abstract
Prostate pathology analysis has been handled remarkably well by deep learning models. However, deep learning models in histopathology still face challenges in effectively modeling fine-grained, object-level features that are critical for a robust cancer assessment. Topological Data Analysis (TDA) has shown promise in addressing these issues but remains underexplored, particularly for whole-slide pathology applications. This is manifested in the nonexistent application of TDA on the slide level; an issue that significantly undermines their practical usability, and the non-utilization of the multi-magnification nature of Whole-slide Images (WSIs). Furthermore, existing studies are limited to small-scale datasets, thus challenging the validity of TDA in histopathology. 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 further propose the Cubic version cPMIL, utilizing the magnification factor in pathology images for filtration and unlocking an improved object-level modeling capability. We validate our approach on two prostate subtyping datasets, comparing against multiple state-of-the-art methods. Our proposed modules are developed offline, can be seamlessly plugged in any MIL pipeline, do not incur any additional expert annotation, operational within clinical limitations, user-friendly, and open-source. We provide a detailed guide for implementing the modules at github.com/ahmadobeid/PMIL.