THIR: Topological Histopathological Image Retrieval

TL;DR

THIR is a novel, training-free CBMIR framework that uses topological data analysis to effectively retrieve histopathological images based on structural patterns, outperforming existing methods.

Contribution

It introduces a topological approach using Betti numbers for image retrieval, eliminating the need for extensive training or annotated datasets.

Findings

Outperforms state-of-the-art supervised and unsupervised methods.

Processes entire dataset in under 20 minutes on a standard CPU.

Operates without supervision, enabling scalable clinical application.

Abstract

According to the World Health Organization, breast cancer claimed the lives of approximately 685,000 women in 2020. Early diagnosis and accurate clinical decision making are critical in reducing this global burden. In this study, we propose THIR, a novel Content-Based Medical Image Retrieval (CBMIR) framework that leverages topological data analysis specifically, Betti numbers derived from persistent homology to characterize and retrieve histopathological images based on their intrinsic structural patterns. Unlike conventional deep learning approaches that rely on extensive training, annotated datasets, and powerful GPU resources, THIR operates entirely without supervision. It extracts topological fingerprints directly from RGB histopathological images using cubical persistence, encoding the evolution of loops as compact, interpretable feature vectors. The similarity retrieval is then performed by computing the distances between these topological descriptors, efficiently returning the top-K most relevant matches. Extensive experiments on the BreaKHis dataset demonstrate that THIR outperforms state of the art supervised and unsupervised methods. It processes the entire dataset in under 20 minutes on a standard CPU, offering a fast, scalable, and training free solution for clinical image retrieval.