A generalizable 3D framework and model for self-supervised learning in medical imaging.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41203891.
- Also identified by DOI 10.1038/s41746-025-02035-w and PMC identifier 12594808.
- Licence recorded as CC BY-NC-ND.
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Abstract
Current self-supervised learning (SSL) 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 pretrain 3DINO-ViT: a general-purpose model for medical imaging, on a ultra-large multimodal dataset of ~100,000 3D scans from over 10 organs. We show 3DINO-ViT outperforms state-of-the-art pretrained models on numerous downstream imaging tasks.