Multi-scale attention subtraction networks and an ultra-wide field retinal hemorrhage dataset

Renkai Wu, Pengchen Liang, Yiqi Huang, Qing Chang* and Huiping Yao*

1. Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
2. Shanghai University, Shanghai, China

News🚀

(2024.10.10) The UWF-RHS dataset is now fully public.🔥

(2024.09.17) Our model code has been uploaded! The UWF-RHS Dataset will provide access links next. Stay tuned!

(2024.09.10) This work has been accepted for early access by IEEE Journal of Biomedical and Health Informatics!🔥

(2024.03.02) Upload the corresponding running code.

(2024.03.02) Manuscript submitted for review. 📃

0. Main Environments.

• python 3.8
• pytorch 1.12.0

1. The proposed datasets (UWF-RHS).
The UWF-RHS dataset is available here. It should be noted:

1. If you use the dataset, please cite the paper: https://doi.org/10.1109/JBHI.2024.3457512
2. The UWF-RHS dataset may only be used for academic research, not for commercial purposes.
3. If you can, please give us a like (Starred) for our GitHub project: https://github.com/wurenkai/UWF-RHS-Dataset-and-MASNet

video.mp4

2. Train the MASNet.

python train.py

• After trianing, you could obtain the outputs in './results/'

3. Test the MASNet.
First, in the test.py file, you should change the address of the checkpoint in 'resume_model' and fill in the location of the test data in 'data_path'.

python test.py

• After testing, you could obtain the outputs in './results/'

Citation

If you find this repository helpful, please consider citing:

@article{wu2024automatic,
  title={Automatic Segmentation of Hemorrhages in the Ultra-wide Field Retina: Multi-scale Attention Subtraction Networks and An Ultra-wide Field Retinal Hemorrhage Dataset},
  author={Wu, Renkai and Liang, Pengchen and Huang, Yiqi and Chang, Qing and Yao, Huiping},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2024},
  publisher={IEEE}
}