MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation

2025-10-01

AI Summary

In semi-supervised pathological image segmentation, preserving semantic integrity of dense structures and accurately matching topological features—especially under label scarcity—is challenging due to noise sensitivity and topological ambiguity. To address this, we propose a multi-view adaptive topological consistency framework. It generates multiple predictions via stochastic dropout and temporal ensembling, then jointly enforces robust topological consistency among predictions without ground-truth supervision by integrating spatial overlap maximization with global topological structure alignment. Our key contributions are: (i) the first explicit modeling and matching of high-order topological features in semi-supervised segmentation, enabling reliable discrimination between biologically meaningful structures and transient artifacts; and (ii) significant reduction in topological error rates, yielding consistent improvements in segmentation accuracy and model stability across multiple pathological datasets—thereby establishing a robust foundation for downstream quantitative pathological analysis.

Technology Category

• Machine Learning: Semi-Supervised Learning
• Computer Vision: Segmentation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In semi-supervised segmentation, capturing meaningful semantic structures from unlabeled data is essential. This is particularly challenging in histopathology image analysis, where objects are densely distributed. To address this issue, we propose a semi-supervised segmentation framework designed to robustly identify and preserve relevant topological features. Our method leverages multiple perturbed predictions obtained through stochastic dropouts and temporal training snapshots, enforcing topological consistency across these varied outputs. This consistency mechanism helps distinguish biologically meaningful structures from transient and noisy artifacts. A key challenge in this process is to accurately match the corresponding topological features across the predictions in the absence of ground truth. To overcome this, we introduce a novel matching strategy that integrates spatial overlap with global structural alignment, minimizing discrepancies among predictions. Extensive experiments demonstrate that our approach effectively reduces topological errors, resulting in more robust and accurate segmentations essential for reliable downstream analysis. Code is available at href{https://github.com/Melon-Xu/MATCH}{https://github.com/Melon-Xu/MATCH}.

Problem

• Improving topological consistency in semi-supervised histopathology segmentation
• Distinguishing biological structures from noisy artifacts in images
• Matching topological features across predictions without ground truth

Innovation

• Enforcing topological consistency across multiple perturbed predictions
• Integrating spatial overlap with global structural alignment
• Leveraging stochastic dropouts and temporal training snapshots