CAD_diagnosis

The implementation of our works:

"A Trusted Lesion-assessment Network for Interpretable Diagnosis of Coronary Artery Disease in Coronary CT Angiography"

"Spatio-Temporal Contrast Network for Data-Efficient Learning of Coronary Artery Disease in Coronary CT Angiography".

Overview

Requirements

The required packages include Python 3.9, PyTorch 1.12, einops, nibabel, numpy, scipy, and torchvision.

Citation

@inproceedings{ma2024spatio,
  title={Spatio-Temporal Contrast Network for Data-Efficient Learning of Coronary Artery Disease in Coronary CT Angiography},
  author={Ma, Xinghua and Zou, Mingye and Fang, Xinyan and Liu, Yang and Luo, Gongning and Wang, Wei and Wang, Kuanquan and Qiu, Zhaowen and Gao, Xin and Li, Shuo},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={645--655},
  year={2024},
  organization={Springer}
}

Acknowledgment

• Detection Transformer: /~https://github.com/facebookresearch/detr
• Data Preprocessing: /~https://github.com/jackyko1991/Multiplanar-Reconstruction