PLS4MIS is an open-source toolbox for partially labeled medical image segmentation.
- This project aims to facilitate research in scenarios where full pixel-wise annotations are expensive or infeasible by providing literature reviews, benchmark implementations, and practical PyTorch code.
- 📁 Focused on partially labeled supervision for 3D medical image segmentation
- 📚 Includes daily-updated literature reviews
- 🛠️ Implements six representative algorithms
- 🧪 Ready-to-run examples and scripts
Some information and download links of the partially labeled learning datasets can be found in this Link.
Some implementations of partially labeled learning methods can be found in this Link.
📖 Literature reviews of partially labeled learning approach for medical image segmentation (PLS4MIS)
| Date | The First and Last Authors | Title | Code | Reference |
|---|---|---|---|---|
| 2025-01 | X. Jiang and X. Yang | Labeled-to-unlabeled distribution alignment for partially-supervised multi-organ medical image segmentation | Code | MedIA2025 |
| 2024-06 | B. Billot and P. Golland | Network conditioning for synergistic learning on partial annotations | Code | MIDL2024 |
| 2024-05 | H. Liu and S. Grbic | COSST: Multi-Organ Segmentation With Partially Labeled Datasets Using Comprehensive Supervisions and Self-Training | None | TMI2024 |
| 2023-09 | Y. Xie and C. Shen | Learning From Partially Labeled Data for Multi-Organ and Tumor Segmentation | Code | TPAMI2023 |
| 2023-06 | X. Liu and S. Yang | CCQ: Cross-Class Query Network for Partially Labeled Organ Segmentation | Code | AAAI2023 |
| 2022-08 | R. Deng and Y. Huo | Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled Data | Code | MIDL2022 |
| 2022-04 | H. Wu and A. Sowmya | Tgnet: A Task-Guided Network Architecture for Multi-Organ and Tumour Segmentation from Partially Labelled Datasets | None | ISBI2022 |
| 2021-09 | L. Fidon and T. Vercauteren | Label-Set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation | Code | MICCAI2021 |
| 2021-05 | G. Shi and SK. Zhou | Marginal loss and exclusion loss for partially supervised multi-organ segmentation | Code | MedIA2021 |
| 2021-03 | J. Zhang and C. Shen | DoDNet: Learning To Segment Multi-Organ and Tumors From Multiple Partially Labeled Datasets | Code | CVPR2021 |
| 2020-11 | X. Fang and P. Yan | Multi-Organ Segmentation Over Partially Labeled Datasets With Multi-Scale Feature Abstraction | Code | TMI2020 |
We welcome contributions, suggestions, and collaborations!
- 📧 Email: [email protected]
- 💬 QQ Group (Chinese): 906808850