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

TL;DR

This paper introduces CSDS, a semi-supervised framework that learns disentangled stain and tissue structure representations for improved gland segmentation in histopathology images, especially with limited labeled data.

Contribution

The novel CSDS framework employs dual student networks with uncertainty estimation and a shared teacher to enhance semi-supervised segmentation of histopathology images.

Findings

Achieves state-of-the-art results on GlaS and CRAG datasets.

Improves Dice scores by up to 1.2% with only 5% labeled data.

Effective disentanglement of stain and structure representations.

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.