Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation

• URL: http://arxiv.org/abs/2507.03923v2
• Date: Sun, 03 Aug 2025 04:09:32 GMT
• Title: Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation
• Authors: Ha-Hieu Pham, Nguyen Lan Vi Vu, Thanh-Huy Nguyen, Ulas Bagci, Min Xu, Trung-Nghia Le, Huy-Hieu Pham,
• Abstract summary: We propose Color-Structure Dual-Student (CSDS) to learn disentangled representations of stain appearance and tissue structure.<n>CSDS comprises two specialized student networks: one trained on stain-augmented inputs to model chromatic variation, and the other on structure-augmented inputs to capture morphological cues.<n> Experiments on the GlaS and CRAG datasets show that CSDS achieves state-of-the-art performance in low-label settings.
• Score: 9.55376798293006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate gland segmentation in histopathology images is essential for cancer diagnosis and prognosis. However, significant variability in Hematoxylin and Eosin (H&E) staining and tissue morphology, combined with limited annotated data, poses major challenges for automated segmentation. To address this, we propose Color-Structure Dual-Student (CSDS), a novel semi-supervised segmentation framework designed to learn disentangled representations of stain appearance and tissue structure. CSDS comprises two specialized student networks: one trained on stain-augmented inputs to model chromatic variation, and the other on structure-augmented inputs to capture morphological cues. A shared teacher network, updated via Exponential Moving Average (EMA), supervises both students through pseudo-labels. To further improve label reliability, we introduce stain-aware and structure-aware uncertainty estimation modules that adaptively modulate the contribution of each student during training. Experiments on the GlaS and CRAG datasets show that CSDS achieves state-of-the-art performance in low-label settings, with Dice score improvements of up to 1.2% on GlaS and 0.7% on CRAG at 5% labeled data, and 0.7% and 1.4% at 10%. Our code and pre-trained models are available at https://github.com/hieuphamha19/CSDS.

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