Awesome 3D Medical Pre-Training [](https://awesome.re)
July 7, 2025 · View on GitHub
- ❤ We provide a comprehensive list of publications about 3D medical pre-training.
- 🔥 Welcome to share the paper and code through the issues.
| title | paper | repo |
|---|---|---|
| How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks? |  |  |
| MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset |  |  |
| Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training |  |  |
| CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection |  |  |
| UniMiSS: Universal Medical Self-supervised Learning via Breaking Dimensionality Barrier |  |  |
| Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis |  | |
| DoDNet: Learning to Segment Multi-Organ and Tumors from Multiple Partially Labeled Datasets |  |  |
| Models Genesis |  |  |
| Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis | | |
| VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis |  |  |
| Large-Scale 3D Medical Image Pre-training with Geometric Context Priors |  |  |
| STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training |  |  |
| HySparK: Hybrid Sparse Masking for Large Scale Medical Image Pre-Training |  |  |
| MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation |  |  |