Skip to content

Latest commit

 

History

History
24 lines (13 loc) · 1 KB

README.md

File metadata and controls

24 lines (13 loc) · 1 KB
Raw

Dense Regression Activation Maps For Lesion Segmentation in CT scans of COVID-19 patients

the implementation of our dense regression activation map algorithm. The algorithm takes a CT image and its corresponding lobe segmentation as the input, generating a lesion segmentation as the output.

Installation

  • Please check /docker_base/DockerFile for the required pacakges to build the docker image. Note that there is another /DockerFile is for build a docker image for grand-challenge algorithm.
  • Regarding DGL library, we suggest you install 0.6.x. 0.4.x cannot be used because of bugs related to the implementation of graph attention networks.

Usage

  • before training, run prepare_data.py to generate lobw-wise chunk images for training.

  • For training, run train.py

  • For testing, run process_pipeline.py

License

MIT