Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911063641948160 |
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| author | Rokuss, Maximilian Hamm, Benjamin Kirchhoff, Yannick Maier-Hein, Klaus |
| author_facet | Rokuss, Maximilian Hamm, Benjamin Kirchhoff, Yannick Maier-Hein, Klaus |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13830 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation Rokuss, Maximilian Hamm, Benjamin Kirchhoff, Yannick Maier-Hein, Klaus Image and Video Processing Computer Vision and Pattern Recognition 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 |
| title | Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.13830 |