Graph-Theoretic Consistency for Robust and Topology-Aware Semi-Supervised Histopathology Segmentation (Student Abstract)
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.
Fields of Study & Subjects
AI in cancer detection
Digital Imaging for Blood Diseases
Gene expression and cancer classification
Publication details
- Journal / source
- Proceedings of the AAAI Conference on Artificial Intelligence
- Publisher
- Association for the Advancement of Artificial Intelligence (AAAI)
- Volume
- 40
- Issue
- 48
- Pages
- 41354–41356
- Publication date
- 14 March 2026
- Language
- EN