Integrating Multi-scale and Multi-filtration Topological Features for Medical Image Classification

TL;DR

This paper introduces a novel topology-guided framework that extracts and integrates multi-scale and multi-filtration persistent topological features into deep learning models, significantly improving medical image classification accuracy and interpretability.

Contribution

It develops a multi-scale, multi-filtration persistent topological feature extraction and integration method, including a vineyard algorithm and cross-attention neural network, for enhanced medical image classification.

Findings

Consistent improvements over state-of-the-art methods on three datasets.

Enhanced recognition of complex anatomical structures.

Improved robustness and interpretability of classification models.

Abstract

Modern deep neural networks have shown remarkable performance in medical image classification. However, such networks either emphasize pixel-intensity features instead of fundamental anatomical structures (e.g., those encoded by topological invariants), or they capture only simple topological features via single-parameter persistence. In this paper, we propose a new topology-guided classification framework that extracts multi-scale and multi-filtration persistent topological features and integrates them into vision classification backbones. For an input image, we first compute cubical persistence diagrams (PDs) across multiple image resolutions/scales. We then develop a ``vineyard'' algorithm that consolidates these PDs into a single, stable diagram capturing signatures at varying granularities, from global anatomy to subtle local irregularities that may indicate early-stage disease. To further exploit richer topological representations produced by multiple filtrations, we design a cross-attention-based neural network that directly processes the consolidated final PDs. The resulting topological embeddings are fused with feature maps from CNNs or Transformers. By integrating multi-scale and multi-filtration topologies into an end-to-end architecture, our approach enhances the model's capacity to recognize complex anatomical structures. Evaluations on three public datasets show consistent, considerable improvements over strong baselines and state-of-the-art methods, demonstrating the value of our comprehensive topological perspective for robust and interpretable medical image classification.