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

• URL: http://arxiv.org/abs/2509.13834v1
• Date: Wed, 17 Sep 2025 09:03:04 GMT
• Title: Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation
• Authors: Nguyen Lan Vi Vu, Thanh-Huy Nguyen, Thien Nguyen, Daisuke Kihara, Tianyang Wang, Xingjian Li, Min Xu,
• Abstract summary: Semi-supervised learning has been employed to alleviate the need for extensive labeled data for histopathology image segmentation.<n>Existing methods struggle with noisy pseudo-labels due to ambiguous gland boundaries and morphological misclassification.<n>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.
• Score: 13.530424405137417
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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