mlsanityData to 7 Oct 2026

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

arXiv 2501.11755 ↗First posted 20 Jan 2025eess.IV cs.CVcited by 26

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Abstract (arXiv)

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.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 28 of its 68 references that are in the map. First posted on arXiv 20 Jan 2025.

Builds on

28 of 68 references.

References · 28

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Citing papers per year: 2025: 8; 2026: 18.

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  1. 2025Comprehensive language-image pre-training for 3D medical image understandingcited by 20
  2. 2025Revisiting 2D Foundation Models for Scalable 3D Medical Image Classificationcited by 9
  3. 2026The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Predictioncited by 2
  4. 2025Multi-encoder nnU-Net outperforms transformer models with self-supervised pretrainingcited by 1
  5. 2025Live(r) Die: Predicting Survival in Colorectal Liver Metastasiscited by 1
  6. 2026M3Net: A Macro-to-Meso-to-Micro Clinical-inspired Hierarchical 3D Network for Pulmonary Nodule Classificationcited by 1
  7. 2026Anatomy Contextualized Adaptation of CT Foundation Modelscited by 1
  8. 2025Multi-Scale Feature Fusion with Image-Driven Spatial Integration for Left Atrium Segmentation from Cardiac MRI Imagescited by 0
  9. 2025Evaluating Vision Language Models (VLMs) for Radiology: A Comprehensive Analysiscited by 0
  10. 2025PRISM: A Framework Harnessing Unsupervised Visual Representations and Textual Prompts for Explainable MACE Survival Prediction from Cardiac Cine MRIcited by 0
  11. 2025Shaken or Stirred? An Analysis of MetaFormer's Token Mixing for Medical Imagingcited by 0
  12. 2026Opportunistic Promptable Segmentation: Leveraging Routine Radiological Annotations to Guide 3D CT Lesion Segmentationcited by 0
14 more
  1. 2026An Automated Radiomics Framework for Postoperative Survival Prediction in Colorectal Liver Metastases using Preoperative MRIcited by 0
  2. 2026Learning Generalizable 3D Medical Image Representations from Mask-Guided Self-Supervisioncited by 0
  3. 2026FDIF: Formula-Driven supervised Learning with Implicit Functions for 3D Medical Image Segmentationcited by 0
  4. 20263D MRI Image Pretraining via Controllable 2D Slice Navigation Taskcited by 0
  5. 2026Knowledge Transfer Scaling Laws for 3D Medical Imagingcited by 0
  6. 2026MultiMedVision: Multi-Modal Medical Vision Frameworkcited by 0
  7. 2026Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelinescited by 0
  8. 2026Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translationcited by 0
  9. 2026Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomographycited by 0
  10. 2026Do Medical Foundation Models Generalize on the African Brain?cited by 0
  11. 2026PatTree: a novel approach for automated creation of multimodal, graph-based patient representations for medical classification taskscited by 0
  12. 2026Do 3D Medical Foundation Models See Through MRI Artifacts? A Controlled Study of Representation Robustnesscited by 0
  13. 2026Representation learning of human cortical folding to reveal long lasting neurodevelopmental signaturescited by 0
  14. 2026AWR-Net: Decoupling Anatomy and Appearance for 3D Fetal Brain Ultrasound Synthesiscited by 0