Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation
Journal:
arXiv
Published Date:
Jul 5, 2025
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.
