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

• URL: http://arxiv.org/abs/2512.07190v1
• Date: Mon, 08 Dec 2025 06:02:02 GMT
• Title: Integrating Multi-scale and Multi-filtration Topological Features for Medical Image Classification
• Authors: Pengfei Gu, Huimin Li, Haoteng Tang, Dongkuan, Xu, Erik Enriquez, DongChul Kim, Bin Fu, Danny Z. Chen,
• Abstract summary: Deep neural networks have shown remarkable performance in medical image classification.<n>We propose a new topology-guided classification framework that extracts multi-scale and multi-filtration persistent topological features.<n>Our approach enhances the model's capacity to recognize complex anatomical structures.
• Score: 20.820287362872975
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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