{"ID":2869776,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13834","arxiv_id":"2509.13834","title":"Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation","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.","short_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, t...","url_abs":"https://arxiv.org/abs/2509.13834","url_pdf":"https://arxiv.org/pdf/2509.13834v1","authors":"[\"Nguyen Lan Vi Vu\",\"Thanh-Huy Nguyen\",\"Thien Nguyen\",\"Daisuke Kihara\",\"Tianyang Wang\",\"Xingjian Li\",\"Min Xu\"]","published":"2025-09-17T09:03:04Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":609718,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2869776,"paper_url":"https://arxiv.org/abs/2509.13834","paper_title":"Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation","repo_url":"https://github.com/vnlvi2k3/Semi-MoE","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}