{"ID":2841258,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12110","arxiv_id":"2511.12110","title":"MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images","abstract":"Despite recent progress in text-prompt-based medical image segmentation, these methods are limited to single-round dialogues and fail to support multi-round reasoning, which is important for medical education scenarios. In this work, we introduce Multi-Round Entity-Level Medical Reasoning Segmentation (MEMR-Seg), a new task that requires generating segmentation masks through multi-round queries with entity-level reasoning, helping learners progressively develop their understanding of medical knowledge. To support this task, we construct MR-MedSeg, a large-scale dataset of 177K multi-round medical segmentation dialogues, featuring entity-based reasoning across rounds. Furthermore, we propose MediRound, an effective baseline model designed for multi-round medical reasoning segmentation. To mitigate the inherent error propagation within the chain-like pipeline of multi-round segmentation, we introduce a lightweight yet effective Judgment \u0026 Correction Mechanism during model inference. Experimental results demonstrate that our method effectively addresses the MEMR-Seg task and outperforms conventional medical referring segmentation methods. The project is available at https://github.com/Edisonhimself/MediRound.","short_abstract":"Despite recent progress in text-prompt-based medical image segmentation, these methods are limited to single-round dialogues and fail to support multi-round reasoning, which is important for medical education scenarios. In this work, we introduce Multi-Round Entity-Level Medical Reasoning Segmentation (MEMR-Seg), a new...","url_abs":"https://arxiv.org/abs/2511.12110","url_pdf":"https://arxiv.org/pdf/2511.12110v4","authors":"[\"Qinyue Tong\",\"Ziqian Lu\",\"Jun Liu\",\"Rui Zuo\",\"Zheming Lu\",\"Yueming Jin\"]","published":"2025-11-15T08:59:21Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":607042,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2841258,"paper_url":"https://arxiv.org/abs/2511.12110","paper_title":"MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images","repo_url":"https://github.com/Edisonhimself/MediRound","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}