mlsanityData to 7 Oct 2026

Med3D: Transfer Learning for 3D Medical Image Analysis

arXiv 1904.00625 ↗First posted 1 Apr 2019cs.CVcited by 282

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Cited by 35 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The performance on deep learning is significantly affected by volume of training data. Models pre-trained from massive dataset such as ImageNet become a powerful weapon for speeding up training convergence and improving accuracy. Similarly, models based on large dataset are important for the development of deep learning in 3D medical images. However, it is extremely challenging to build a sufficiently large dataset due to difficulty of data acquisition and annotation in 3D medical imaging. We aggregate the dataset from several medical challenges to build 3DSeg-8 dataset with diverse modalities, target organs, and pathologies. To extract general medical three-dimension (3D) features, we design a heterogeneous 3D network called Med3D to co-train multi-domain 3DSeg-8 so as to make a series of pre-trained models. We transfer Med3D pre-trained models to lung segmentation in LIDC dataset, pulmonary nodule classification in LIDC dataset and liver segmentation on LiTS challenge. Experiments show that the Med3D can accelerate the training convergence speed of target 3D medical tasks 2 times compared with model pre-trained on Kinetics dataset, and 10 times compared with training from scratch as well as improve accuracy ranging from 3% to 20%. Transferring our Med3D model on state-the-of-art DenseASPP segmentation network, in case of single model, we achieve 94.6% Dice coefficient which approaches the result of top-ranged algorithms on the LiTS challenge.

Key facts. Cited by 282 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Apr 2019.

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Citing papers per year: 2021: 23; 2022: 27; 2023: 53; 2024: 52; 2025: 71; 2026: 56.

282 papers.

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  1. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 418
  2. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  3. 2022Brain Imaging Generation with Latent Diffusion Modelscited by 267
  4. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
  5. 2023On the Challenges and Perspectives of Foundation Models for Medical Image Analysiscited by 135
  6. 2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformercited by 124
  7. 2022LViT: Language meets Vision Transformer in Medical Image Segmentationcited by 110
  8. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  9. 2021On the Role of Neural Collapse in Transfer Learningcited by 100
  10. 2023Conditional Diffusion Models for Semantic 3D Brain MRI Synthesiscited by 85
  11. 2022Masked Image Modeling Advances 3D Medical Image Analysiscited by 71
  12. 2021Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learningcited by 68
38 more
  1. 2023Generative AI for Medical Imaging: extending the MONAI Frameworkcited by 68
  2. 2022Delving into Masked Autoencoders for Multi-Label Thorax Disease Classificationcited by 65
  3. 2024AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarkingcited by 64
  4. 2023Large AI Models in Health Informatics: Applications, Challenges, and the Futurecited by 61
  5. 2023LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matchingcited by 60
  6. 2024WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesiscited by 56
  7. 2022Transfer Learning with Deep Tabular Modelscited by 52
  8. 2023UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learnercited by 46
  9. 2023MultiTalent: A Multi-Dataset Approach to Medical Image Segmentationcited by 45
  10. 2023MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Imagescited by 41
  11. 2023CLIP in Medical Imaging: A Surveycited by 40
  12. 2023A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imagingcited by 39
  13. 2021Supervised Transfer Learning at Scale for Medical Imagingcited by 38
  14. 20243D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generationcited by 37
  15. 2023Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentationcited by 32
  16. 2024Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomographycited by 31
  17. 2024Learning General-Purpose Biomedical Volume Representations using Randomized Synthesiscited by 27
  18. 2023DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentationcited by 26
  19. 2024M4oE: A Foundation Model for Medical Multimodal Image Segmentation with Mixture of Expertscited by 26
  20. 2023Neuralizer: General Neuroimage Analysis without Re-Trainingcited by 24
  21. 2025Pillar-0: A New Frontier for Radiology Foundation Modelscited by 24
  22. 2022Prompt Tuning for Parameter-efficient Medical Image Segmentationcited by 21
  23. 2021Label-Assemble: Leveraging Multiple Datasets with Partial Labelscited by 20
  24. 2025How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?cited by 19
  25. 2022Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classificationcited by 18
  26. 2022Learning from partially labeled data for multi-organ and tumor segmentationcited by 18
  27. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIscited by 18
  28. 2023Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentationcited by 17
  29. 2021Free-form tumor synthesis in computed tomography images via richer generative adversarial networkcited by 16
  30. 2021Medical Transformer: Universal Brain Encoder for 3D MRI Analysiscited by 16
  31. 2021Medical Image Segmentation with 3D Convolutional Neural Networks: A Surveycited by 16
  32. 2022Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image Segmentationcited by 16
  33. 2022Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clusteringcited by 16
  34. 2024CMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal Representation Learning for AD classificationcited by 15
  35. 2024Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Modelcited by 15
  36. 2024Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challengecited by 14
  37. 2024PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentationcited by 14
  38. 2024Deep Generative Models for 3D Medical Image Synthesiscited by 14

50 most cited of 282 All 282 (JSON)