Update README.md

Author: Luoxd1996

README.md

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 # Semi-supervised-learning-for-medical-image-segmentation.
-* **[New], We are reformatting the codebase to support the 5-fold cross-validation and randomly select labeled cases, the reformatted methods in this [Branch](https://github.com/HiLab-git/SSL4MIS/tree/cross_val_dev)**. 
+* **[New] We have transferred to a new topic about active learning and source-free domain adaptation for medical image analysis, which may be closer to the real clinical requirement. The new benchmark is [here](https://github.com/whq-xxh/ADA4MIA).**
+* We are reformatting the codebase to support the 5-fold cross-validation and randomly select labeled cases, the reformatted methods in this [Branch](https://github.com/HiLab-git/SSL4MIS/tree/cross_val_dev). 
 * Recently, semi-supervised image segmentation has become a hot topic in medical image computing, unfortunately, there are only a few open-source codes and datasets, since the privacy policy and others. For easy evaluation and fair comparison, we are trying to build a semi-supervised medical image segmentation benchmark to boost the semi-supervised learning research in the medical image computing community. **If you are interested, you can push your implementations or ideas to this repo or contact [me](https://luoxd1996.github.io/) at any time**. 
 * This repo has re-implemented these semi-supervised methods (with some modifications for semi-supervised medical image segmentation, more details please refer to these original works): (1) [Mean Teacher](https://papers.nips.cc/paper/6719-mean-teachers-are-better-role-models-weight-averaged-consistency-targets-improve-semi-supervised-deep-learning-results.pdf); (2) [Entropy Minimization](https://openaccess.thecvf.com/content_CVPR_2019/papers/Vu_ADVENT_Adversarial_Entropy_Minimization_for_Domain_Adaptation_in_Semantic_Segmentation_CVPR_2019_paper.pdf); (3) [Deep Adversarial Networks](https://link.springer.com/chapter/10.1007/978-3-319-66179-7_47); (4) [Uncertainty Aware Mean Teacher](https://arxiv.org/pdf/1907.07034.pdf); (5) [Interpolation Consistency Training](https://arxiv.org/pdf/1903.03825.pdf); (6) [Uncertainty Rectified Pyramid Consistency](https://arxiv.org/pdf/2012.07042.pdf); (7) [Cross Pseudo Supervision](https://arxiv.org/abs/2106.01226); (8) [Cross Consistency Training](https://openaccess.thecvf.com/content_CVPR_2020/papers/Ouali_Semi-Supervised_Semantic_Segmentation_With_Cross-Consistency_Training_CVPR_2020_paper.pdf); (9) [Deep Co-Training](https://openaccess.thecvf.com/content_ECCV_2018/papers/Siyuan_Qiao_Deep_Co-Training_for_ECCV_2018_paper.pdf); (10) [Cross Teaching between CNN and Transformer](https://arxiv.org/pdf/2112.04894.pdf); (11) [FixMatch](https://arxiv.org/abs/2001.07685); (12) [Regularized Dropout](https://proceedings.neurips.cc/paper/2021/file/5a66b9200f29ac3fa0ae244cc2a51b39-Paper.pdf). In addition, several backbones networks (both 2D and 3D) are also supported in this repo, such as **UNet, nnUNet, VNet, AttentionUNet, ENet, Swin-UNet, etc**.