MORE: Multi-Organ Medical Image REconstruction Dataset
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917050705772544 |
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| author | Wu, Shaokai Guo, Yapan Ji, Yanbiao Tong, Jing Lu, Yuxiang Li, Mei Huang, Suizhi Ding, Yue Lu, Hongtao |
| author_facet | Wu, Shaokai Guo, Yapan Ji, Yanbiao Tong, Jing Lu, Yuxiang Li, Mei Huang, Suizhi Ding, Yue Lu, Hongtao |
| contents | CT reconstruction provides radiologists with images for diagnosis and treatment, yet current deep learning methods are typically limited to specific anatomies and datasets, hindering generalization ability to unseen anatomies and lesions. To address this, we introduce the Multi-Organ medical image REconstruction (MORE) dataset, comprising CT scans across 9 diverse anatomies with 15 lesion types. This dataset serves two key purposes: (1) enabling robust training of deep learning models on extensive, heterogeneous data, and (2) facilitating rigorous evaluation of model generalization for CT reconstruction. We further establish a strong baseline solution that outperforms prior approaches under these challenging conditions. Our results demonstrate that: (1) a comprehensive dataset helps improve the generalization capability of models, and (2) optimization-based methods offer enhanced robustness for unseen anatomies. The MORE dataset is freely accessible under CC-BY-NC 4.0 at our project page https://more-med.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26759 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MORE: Multi-Organ Medical Image REconstruction Dataset Wu, Shaokai Guo, Yapan Ji, Yanbiao Tong, Jing Lu, Yuxiang Li, Mei Huang, Suizhi Ding, Yue Lu, Hongtao Image and Video Processing Computer Vision and Pattern Recognition Multimedia CT reconstruction provides radiologists with images for diagnosis and treatment, yet current deep learning methods are typically limited to specific anatomies and datasets, hindering generalization ability to unseen anatomies and lesions. To address this, we introduce the Multi-Organ medical image REconstruction (MORE) dataset, comprising CT scans across 9 diverse anatomies with 15 lesion types. This dataset serves two key purposes: (1) enabling robust training of deep learning models on extensive, heterogeneous data, and (2) facilitating rigorous evaluation of model generalization for CT reconstruction. We further establish a strong baseline solution that outperforms prior approaches under these challenging conditions. Our results demonstrate that: (1) a comprehensive dataset helps improve the generalization capability of models, and (2) optimization-based methods offer enhanced robustness for unseen anatomies. The MORE dataset is freely accessible under CC-BY-NC 4.0 at our project page https://more-med.github.io/ |
| title | MORE: Multi-Organ Medical Image REconstruction Dataset |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2510.26759 |