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.
@article{arxiv.2501.11755,
title = {A generalizable 3D framework and model for self-supervised learning in medical imaging},
author = {Tony Xu and Sepehr Hosseini and Chris Anderson and Anthony Rinaldi and Rahul G. Krishnan and Anne L. Martel and Maged Goubran},
journal= {arXiv preprint arXiv:2501.11755},
year = {2025}
}