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<p>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.</p>

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A generalizable 3D framework and model for self-supervised learning in medical imaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月7日

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  • Tony Xu,
  • Sepehr Hosseini,
  • Chris Anderson,
  • Anthony Rinaldi,
  • Rahul G. Krishnan,
  • Anne L. Martel,
  • Maged Goubran

摘要

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.