😄 This project is the pytorch implemention of

$M^3$

  😆 Our experimental platform is configured with One RTX3090 (cuda>=11.0);

  😊 Currently, this code is avaliable for proposed dataset FCS and public dataset CardiacUDA;

      👀 The code is now available at:       ..\data\detus_dataset.py

      👀 The dataset is coming soon.：

1. You need to build the relevant environment first, please refer to : requirements.yaml

2. Install Environment:

   conda env create -f requirements.yaml
   

• We recommend you to use Anaconda to establish an independent virtual environment, and python > = 3.8.3;

• This project provides the use case of Unsupervised Domain Adaptive Fetal Cardiac Structure Detection task;

• The hyper parameters setting of the dataset can be found in the utils/config.py, where you could do the parameters modification;

• For different tasks, the composition of data sets have significant different, so there is no repetition in this file;

  1. Download & Unzip the dataset.

     The FCS dataset is composed as: /Hospital1 & /Hospital2 & Hospital3.

  2. The source code of loading the FCS dataset exist in path :

     ..\data\fetus_dataset.py
     and modify the dataset path in
     ..\utils/config.py

  3. Set the parameters about GPU_id, source domain,target domain and slice etc in utils/config.py

• Dataset access can be obtained by contacting hospital staff (doc.liangbc@gmail.com) and asking for a license.

1. In this framework, after the parameters are configured in the file utils/config.py and train.py , you only need to use the command:

   python train.py

2. You are also able to start distributed training.

   • Note: Please set the number of graphics cards you need and their id in parameter "enable_GPUs_id".

   python -m torch.distributed.launch --nproc_per_node=4 train.py

1. Download the checkpoint in table below.

2. Update the test weight path in config.py.

@inproceedings{pu2024m3,
  title={M3-UDA: A New Benchmark for Unsupervised Domain Adaptive Fetal Cardiac Structure Detection},
  author={Pu, Bin and Wang, Liwen and Yang, Jiewen and He, Guannan and Dong, Xingbo and Li, Shengli and Tan, Ying and Chen, Ming and Jin, Zhe and Li, Kenli and others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11621--11630},
  year={2024}
}

• https://github.com/CityU-AIM-Group/SIGMA