{"ID":2859334,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.05899","arxiv_id":"2510.05899","title":"Efficient Universal Models for Medical Image Segmentation via Weakly Supervised In-Context Learning","abstract":"Universal models for medical image segmentation, such as interactive and in-context learning (ICL) models, offer strong generalization but require extensive annotations. Interactive models need repeated user prompts for each image, while ICL relies on dense, pixel-level labels. To address this, we propose Weakly Supervised In-Context Learning (WS-ICL), a new ICL paradigm that leverages weak prompts (e.g., bounding boxes or points) instead of dense labels for context. This approach significantly reduces annotation effort by eliminating the need for fine-grained masks and repeated user prompting for all images. We evaluated the proposed WS-ICL model on three held-out benchmarks. Experimental results demonstrate that WS-ICL achieves performance comparable to regular ICL models at a significantly lower annotation cost. In addition, WS-ICL is highly competitive even under the interactive paradigm. These findings establish WS-ICL as a promising step toward more efficient and unified universal models for medical image segmentation. Our code and model are publicly available at https://github.com/jiesihu/Weak-ICL.","short_abstract":"Universal models for medical image segmentation, such as interactive and in-context learning (ICL) models, offer strong generalization but require extensive annotations. Interactive models need repeated user prompts for each image, while ICL relies on dense, pixel-level labels. To address this, we propose Weakly Superv...","url_abs":"https://arxiv.org/abs/2510.05899","url_pdf":"https://arxiv.org/pdf/2510.05899v2","authors":"[\"Jiesi Hu\",\"Yanwu Yang\",\"Zhiyu Ye\",\"Jinyan Zhou\",\"Jianfeng Cao\",\"Hanyang Peng\",\"Ting Ma\"]","published":"2025-10-07T13:07:27Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":608630,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2859334,"paper_url":"https://arxiv.org/abs/2510.05899","paper_title":"Efficient Universal Models for Medical Image Segmentation via Weakly Supervised In-Context Learning","repo_url":"https://github.com/jiesihu/Weak-ICL","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}