xmed-lab / AdaCon

IEEE TMI 2021: AdaCon: Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment

IEEE TMI 2021: AdaCon: Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment

This is the implementation of AdaCon on the EchoNet-Dynamic Dataset for the paper "AdaCon: Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment" (IEEE TMI).

Data

Researchers can request the EchoNet-Dynamic dataset at https://echonet.github.io/dynamic/ and set the directory path in the configuration file, echonet.cfg.

Environment

It is recommended to use PyTorch conda environments for running the program. A requirements file has been included.

Training and Testing

The code must first be installed by running

pip install --user .

under the adacon directory. To train the model from scratch, run:

echonet video --frames=32 --model_name=r2plus1d_18 --period=2 --batch_size=20 --run_test --output=training_output

Pretrained Model

A trained version of the model can be downloaded from https://hkustconnect-my.sharepoint.com/:u:/g/personal/wdaiaj_connect_ust_hk/EXu95kAzcitGibTOWxwSmDEBKIAia3H8Dw5CbGVDsPbWBg?e=QBzdD6

Inference with the trained model can be run using

echonet video --frames=32 --model_name=r2plus1d_18 --period=2 --batch_size=20 --run_test --output=training_output --weights=<PATH TO MODEL> --num_epochs=0

	MAE	RMSE	R2
AdaCon	3.86	5.07	82.8%

Notes

• Contact: DAI Weihang ([e-mail])

Citation

If this code is useful for your research, please consider citing:

@article{dai2021adaptive,
  title={Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment},
  author={Dai, Weihang and Li, Xiaomeng and Chiu, Wan Hang Keith and Kuo, Michael D and Cheng, Kwang-Ting},
  journal={IEEE Transactions on Medical Imaging},
  year={2021},
  publisher={IEEE}
}