A generalizable 3D framework and model for self-supervised learning in medical imaging
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917898427039744 |
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| author | Xu, Tony Hosseini, Sepehr Anderson, Chris Rinaldi, Anthony Krishnan, Rahul G. Martel, Anne L. Goubran, Maged |
| author_facet | Xu, Tony Hosseini, Sepehr Anderson, Chris Rinaldi, Anthony Krishnan, Rahul G. Martel, Anne L. Goubran, Maged |
| contents | Current self-supervised learning methods for 3D medical imaging rely on simple pretext formulations and organ- or modality-specific datasets, limiting their generalizability and scalability. We present 3DINO, a cutting-edge SSL method adapted to 3D datasets, and use it to pretrain 3DINO-ViT: a general-purpose medical imaging model, on an exceptionally large, multimodal, and multi-organ dataset of ~100,000 3D medical imaging scans from over 10 organs. We validate 3DINO-ViT using extensive experiments on numerous medical imaging segmentation and classification tasks. Our results demonstrate that 3DINO-ViT generalizes across modalities and organs, including out-of-distribution tasks and datasets, outperforming state-of-the-art methods on the majority of evaluation metrics and labeled dataset sizes. Our 3DINO framework and 3DINO-ViT will be made available to enable research on 3D foundation models or further finetuning for a wide range of medical imaging applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11755 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A generalizable 3D framework and model for self-supervised learning in medical imaging Xu, Tony Hosseini, Sepehr Anderson, Chris Rinaldi, Anthony Krishnan, Rahul G. Martel, Anne L. Goubran, Maged Image and Video Processing Computer Vision and Pattern Recognition Current self-supervised learning methods for 3D medical imaging rely on simple pretext formulations and organ- or modality-specific datasets, limiting their generalizability and scalability. We present 3DINO, a cutting-edge SSL method adapted to 3D datasets, and use it to pretrain 3DINO-ViT: a general-purpose medical imaging model, on an exceptionally large, multimodal, and multi-organ dataset of ~100,000 3D medical imaging scans from over 10 organs. We validate 3DINO-ViT using extensive experiments on numerous medical imaging segmentation and classification tasks. Our results demonstrate that 3DINO-ViT generalizes across modalities and organs, including out-of-distribution tasks and datasets, outperforming state-of-the-art methods on the majority of evaluation metrics and labeled dataset sizes. Our 3DINO framework and 3DINO-ViT will be made available to enable research on 3D foundation models or further finetuning for a wide range of medical imaging applications. |
| title | A generalizable 3D framework and model for self-supervised learning in medical imaging |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.11755 |