Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation


Nguyen Lan Vi Vu (Ho Chi Minh City University of Technology), Thanh-Huy Nguyen (Carnegie Mellon University), Thien Nguyen (Posts and Telecommunications Institute of Technology), Daisuke Kihara (Purdue University), Tianyang Wang (University of Alabama at Birmingham), Xingjian Li (Carnegie Mellon University), Min Xu (Carnegie Mellon University)
The 35th British Machine Vision Conference
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Abstract

Semi-supervised learning has been employed to alleviate the need for extensive labeled data for histopathology image segmentation, but existing methods struggle with noisy pseudo-labels due to ambiguous gland boundaries and morphological misclassification. This paper introduces Semi-MOE, to the best of our knowledge, the first multi-task Mixture-of-Experts framework for semi-supervised histopathology image segmentation. Our approach leverages three specialized expert networks: A main segmentation expert, a signed distance field regression expert, and a boundary prediction expert, each dedicated to capturing distinct morphological features. Subsequently, the Multi-Gating Pseudo-labeling module dynamically aggregates expert features, enabling a robust fuse-and-refine pseudo-labeling mechanism. Furthermore, to eliminate manual tuning while dynamically balancing multiple learning objectives, we propose an Adaptive Multi-Objective Loss. Extensive experiments on GlaS and CRAG benchmarks show that our method outperforms state-of-the-art approaches in low-label settings, highlighting the potential of MoE-based architectures in advancing semi-supervised segmentation. Our code is available at https://github.com/vnlvi2k3/Semi-MoE.

Citation

@inproceedings{Vu_2025_BMVC,
author    = {Nguyen Lan Vi Vu and Thanh-Huy Nguyen and Thien Nguyen and Daisuke Kihara and Tianyang Wang and Xingjian Li and Min Xu},
title     = {Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_940/paper.pdf}
}


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