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

2025-12-08

AI Summary

Existing medical image classification methods either neglect anatomical structures—such as topological invariants—or capture only simplistic topological features via single-parameter persistence. To address this, we propose the first end-to-end multi-scale, multi-filter topologically guided framework: it computes multi-resolution persistent homology on cubical complexes, integrates multi-scale persistence diagrams using the vineyard algorithm, and employs a cross-attention network to fuse multi-filter topological features; the framework is plug-and-play compatible with CNN or Transformer backbones. Evaluated on three public medical image datasets, our method significantly outperforms strong baselines and state-of-the-art models. Results demonstrate that multi-scale, multi-filter topological representations substantially enhance classification robustness, interpretability, and generalization capability.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Integrates multi-scale topological features for medical image classification
• Enhances recognition of complex anatomical structures using persistent homology
• Improves interpretability and robustness in disease detection from images

Innovation

• Multi-scale persistent topological features extraction
• Vineyard algorithm consolidates diagrams across scales
• Cross-attention network fuses multi-filtration topology with CNNs/Transformers