{"ID":2893056,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.13830","arxiv_id":"2507.13830","title":"Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation","abstract":"We introduce the first publicly available breast MRI dataset with explicit left and right breast segmentation labels, encompassing more than 13,000 annotated cases. Alongside this dataset, we provide a robust deep-learning model trained for left-right breast segmentation. This work addresses a critical gap in breast MRI analysis and offers a valuable resource for the development of advanced tools in women's health. The dataset and trained model are publicly available at: www.github.com/MIC-DKFZ/BreastDivider","short_abstract":"We introduce the first publicly available breast MRI dataset with explicit left and right breast segmentation labels, encompassing more than 13,000 annotated cases. Alongside this dataset, we provide a robust deep-learning model trained for left-right breast segmentation. This work addresses a critical gap in breast MR...","url_abs":"https://arxiv.org/abs/2507.13830","url_pdf":"https://arxiv.org/pdf/2507.13830v1","authors":"[\"Maximilian Rokuss\",\"Benjamin Hamm\",\"Yannick Kirchhoff\",\"Klaus Maier-Hein\"]","published":"2025-07-18T11:39:25Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[]","has_code":false}