Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation
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arXiv
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| Format: | Preprint |
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2025
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| author | Pham, Ha-Hieu Vu, Nguyen Lan Vi Nguyen, Thanh-Huy Bagci, Ulas Xu, Min Le, Trung-Nghia Pham, Huy-Hieu |
| author_facet | Pham, Ha-Hieu Vu, Nguyen Lan Vi Nguyen, Thanh-Huy Bagci, Ulas Xu, Min Le, Trung-Nghia Pham, Huy-Hieu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03923 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation Pham, Ha-Hieu Vu, Nguyen Lan Vi Nguyen, Thanh-Huy Bagci, Ulas Xu, Min Le, Trung-Nghia Pham, Huy-Hieu Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2507.03923 |