Topological Feature Extraction for Interpretable Cancer Tissue Classification

Traditional deep learning methods for histopathological analysis suffer from a lack of interpretability, which limits their use in the clinic despite their high accuracy. This paper proposes a Topological Data Analysis (TDA) framework for interpretable colorectal cancer tissue classification. We used persistent homology to extract topological features from 5000 histological images representing eight tissue classes, combining persistence landscapes with Support Vector Machine (SVM) classification. This method achieved an overall accuracy rate of 82.70%, while providing biologically interpretable features that are directly related to tissue morphology. Topological features successfully represented cellular connectivity as well as structural patterns, enabling perfect classification of morphologically distinct tissue pairs. This research demonstrates that topological data analysis (TDA) represents a promising alternative to non-transparent methods, offering competitive efficiency while ensuring interpretability, a crucial aspect for its clinical integration in computational pathology.