{"ID":2825185,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.21584","arxiv_id":"2512.21584","title":"UltraLBM-UNet: Ultralight Bidirectional Mamba-based Model for Skin Lesion Segmentation","abstract":"Skin lesion segmentation is a crucial step in dermatology for guiding clinical decision-making. However, existing methods for accurate, robust, and resource-efficient lesion analysis have limitations, including low performance and high computational complexity. To address these limitations, we propose UltraLBM-UNet, a lightweight U-Net variant that integrates a bidirectional Mamba-based global modeling mechanism with multi-branch local feature perception. The proposed architecture integrates efficient local feature injection with bidirectional state-space modeling, enabling richer contextual interaction across spatial dimensions while maintaining computational compactness suitable for point-of-care deployment. Extensive experiments on the ISIC 2017, ISIC 2018, and PH2 datasets demonstrate that our model consistently achieves state-of-the-art segmentation accuracy, outperforming existing lightweight and Mamba counterparts with only 0.034M parameters and 0.060 GFLOPs. In addition, we introduce a hybrid knowledge distillation strategy to train an ultra-compact student model, where the distilled variant UltraLBM-UNet-T, with only 0.011M parameters and 0.019 GFLOPs, achieves competitive segmentation performance. These results highlight the suitability of UltraLBM-UNet for point-of-care deployment, where accurate and robust lesion analyses are essential. The source code is publicly available at https://github.com/LinLinLin-X/UltraLBM-UNet.","short_abstract":"Skin lesion segmentation is a crucial step in dermatology for guiding clinical decision-making. However, existing methods for accurate, robust, and resource-efficient lesion analysis have limitations, including low performance and high computational complexity. To address these limitations, we propose UltraLBM-UNet, a...","url_abs":"https://arxiv.org/abs/2512.21584","url_pdf":"https://arxiv.org/pdf/2512.21584v1","authors":"[\"Linxuan Fan\",\"Juntao Jiang\",\"Weixuan Liu\",\"Zhucun Xue\",\"Jiajun Lv\",\"Jiangning Zhang\",\"Yong Liu\"]","published":"2025-12-25T09:05:02Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":605648,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2825185,"paper_url":"https://arxiv.org/abs/2512.21584","paper_title":"UltraLBM-UNet: Ultralight Bidirectional Mamba-based Model for Skin Lesion Segmentation","repo_url":"https://github.com/LinLinLin-X/UltraLBM-UNet","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}