A generalizable 3D framework and model for self-supervised learning in medical imaging
Journal:
arXiv
Published Date:
Jan 20, 2025
Abstract
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.
