Topological Conditioning for Mammography Models via a Stable Wavelet-Persistence Vectorization

TL;DR

This paper introduces a topological conditioning method using wavelet persistence vectors to enhance mammography model performance across diverse datasets, significantly improving accuracy especially with limited training data.

Contribution

It presents a novel topological data analysis technique that stabilizes and enriches mammography models, improving cross-scanner and cross-population generalization.

Findings

AUC increased from 0.55 to 0.75 on INbreast dataset.

Wavelet persistence channels improve model robustness.

Method demonstrates effectiveness across multiple international cohorts.

Abstract

Breast cancer is the most commonly diagnosed cancer in women and a leading cause of cancer death worldwide. Screening mammography reduces mortality, yet interpretation still suffers from substantial false negatives and false positives, and model accuracy often degrades when deployed across scanners, modalities, and patient populations. We propose a simple conditioning signal aimed at improving external performance based on a wavelet based vectorization of persistent homology. Using topological data analysis, we summarize image structure that persists across intensity thresholds and convert this information into spatial, multi scale maps that are provably stable to small intensity perturbations. These maps are integrated into a two stage detection pipeline through input level channel concatenation. The model is trained and validated on the CBIS DDSM digitized film mammography cohort from the United States and evaluated on two independent full field digital mammography cohorts from Portugal (INbreast) and China (CMMD), with performance reported at the patient level. On INbreast, augmenting ConvNeXt Tiny with wavelet persistence channels increases patient level AUC from 0.55 to 0.75 under a limited training budget.