Graph-Theoretic Consistency for Robust and Topology-Aware Semi-Supervised Histopathology Segmentation

• URL: http://arxiv.org/abs/2509.22689v2
• Date: Wed, 29 Oct 2025 02:47:25 GMT
• Title: Graph-Theoretic Consistency for Robust and Topology-Aware Semi-Supervised Histopathology Segmentation
• Authors: Ha-Hieu Pham, Minh Le, Han Huynh, Nguyen Quoc Khanh Le, Huy-Hieu Pham,
• Abstract summary: Semi-supervised semantic segmentation (SSSS) is vital in computational pathology, where dense annotations are costly and limited.<n>We propose Topology Graph Consistency (TGC), a framework that integrates graph-theoretic constraints by aligning Laplacian spectra, component counts, and adjacency statistics between prediction graphs and references.
• Score: 2.547516931540122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semi-supervised semantic segmentation (SSSS) is vital in computational pathology, where dense annotations are costly and limited. Existing methods often rely on pixel-level consistency, which propagates noisy pseudo-labels and produces fragmented or topologically invalid masks. We propose Topology Graph Consistency (TGC), a framework that integrates graph-theoretic constraints by aligning Laplacian spectra, component counts, and adjacency statistics between prediction graphs and references. This enforces global topology and improves segmentation accuracy. Experiments on GlaS and CRAG demonstrate that TGC achieves state-of-the-art performance under 5-10% supervision and significantly narrows the gap to full supervision.

Related papers

• GAFR-Net: A Graph Attention and Fuzzy-Rule Network for Interpretable Breast Cancer Image Classification [0.0]
  We propose GAFRNet, a robust and interpretable Graph Attention and FuzzyRule Network for histopathology image classification.<n>We show that GAFR-Net consistently outperforms various state-of-the-art methods across multiple magnifications and classification tasks.<n>These results validate the superior generalization and practical utility of GAFR-Net as a reliable decision-support tool for weakly supervised medical image analysis.
  arXiv  Detail & Related papers  (2026-02-10T01:25:57Z)
• Contrastive Graph Modeling for Cross-Domain Few-Shot Medical Image Segmentation [58.41482540044918]
  Cross-domain few-shot medical image segmentation (CD-FSMIS) offers a promising and data-efficient solution for medical applications.<n>We present Contrastive Graph Modeling (C-Graph), a framework that leverages the structural consistency of medical images as a reliable domain-transferable prior.
  arXiv  Detail & Related papers  (2025-12-25T14:00:17Z)
• HTG-GCL: Leveraging Hierarchical Topological Granularity from Cellular Complexes for Graph Contrastive Learning [18.97546255767921]
  Graph contrastive learning aims to learn discriminative semantic invariance by contrasting different views of the same graph that share critical topological patterns.<n>We introduce Hierarchical Topological Granularity Graph Contrastive Learning (HTG-GCL), a novel framework that leverages transformations of the same graph to generate multi-scale ring-based cellular complexes.
  arXiv  Detail & Related papers  (2025-11-30T09:09:42Z)
• Robust Graph Condensation via Classification Complexity Mitigation [61.22258715077984]
  Graph condensation is an intrinsic-dimension-reducing process, synthesizing a condensed graph with lower classification complexity.<n>We introduce three graph data manifold learning modules that guide the condensed graph to lie within a smooth, low-dimensional manifold.<n>Experiments demonstrate the robustness of ModelName across diverse attack scenarios.
  arXiv  Detail & Related papers  (2025-10-30T12:55:21Z)
• MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation [15.740955468843035]
  We propose a semi-supervised segmentation framework designed to robustly identify and preserve relevant topological features.<n>This consistency mechanism helps distinguish biologically meaningful structures from transient and noisy artifacts.<n>We introduce a novel matching strategy that integrates spatial overlap with global structural alignment.
  arXiv  Detail & Related papers  (2025-10-02T00:08:28Z)
• CRoC: Context Refactoring Contrast for Graph Anomaly Detection with Limited Supervision [11.139587480845144]
  We propose Context Refactoring Contrast (CRoC), a framework that trains Graph Neural Networks (GNNs) for Graph Anomaly Detection (GAD)<n>CRoC exploits the class imbalance inherent in GAD to leverage limited labeled and abundant unlabeled data.<n>In the training stage, CRoC is further integrated with the contrastive learning paradigm. This allows GNNs to effectively harness unlabeled data during training, producing richer, more discnative node embeddings.
  arXiv  Detail & Related papers  (2025-08-17T08:05:17Z)
• Hallucination Detection in LLMs with Topological Divergence on Attention Graphs [60.83579255387347]
  Hallucination, i.e., generating factually incorrect content, remains a critical challenge for large language models.<n>We introduce TOHA, a TOpology-based HAllucination detector in the RAG setting.
  arXiv  Detail & Related papers  (2025-04-14T10:06:27Z)
• From Pixels to Histopathology: A Graph-Based Framework for Interpretable Whole Slide Image Analysis [81.19923502845441]
  We develop a graph-based framework that constructs WSI graph representations.<n>We build tissue representations (nodes) that follow biological boundaries rather than arbitrary patches.<n>In our method's final step, we solve the diagnostic task through a graph attention network.
  arXiv  Detail & Related papers  (2025-03-14T20:15:04Z)
• Beyond Message Passing: Neural Graph Pattern Machine [50.78679002846741]
  We introduce the Neural Graph Pattern Machine (GPM), a novel framework that bypasses message passing by learning directly from graph substructures.<n>GPM efficiently extracts, encodes, and prioritizes task-relevant graph patterns, offering greater expressivity and improved ability to capture long-range dependencies.
  arXiv  Detail & Related papers  (2025-01-30T20:37:47Z)
• Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning [47.74244053386216]
  Graph condensation is a promising solution to synthesize a compact, substitute graph of the large-scale original graph. We introduce Contrastive Graph Condensation (CTGC), which adopts a self-supervised surrogate task to extract critical, causal information from the original graph. CTGC excels in handling various downstream tasks with a limited number of labels, consistently outperforming state-of-the-art GC methods.
  arXiv  Detail & Related papers  (2024-11-26T03:01:22Z)
• Topograph: An efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation [78.54656076915565]
  Topological correctness plays a critical role in many image segmentation tasks.<n>Most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological accuracy.<n>We propose a novel, graph-based framework for topologically accurate image segmentation.
  arXiv  Detail & Related papers  (2024-11-05T16:20:14Z)
• Tensor-Fused Multi-View Graph Contrastive Learning [12.412040359604163]
  Graph contrastive learning (GCL) has emerged as a promising approach to enhance graph neural networks' (GNNs) ability to learn rich representations from unlabeled graph-structured data.<n>Current GCL models face challenges with computational demands and limited feature utilization.<n>We propose TensorMV-GCL, a novel framework that integrates extended persistent homology with GCL representations and facilitates multi-scale feature extraction.
  arXiv  Detail & Related papers  (2024-10-20T01:40:12Z)
• Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs [11.315178576537768]
  We propose SegGini, a weakly supervised semantic segmentation method using graphs. SegGini segment arbitrary and large images, scaling from tissue microarray (TMA) to whole slide image (WSI)
  arXiv  Detail & Related papers  (2021-03-04T16:04:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.