Novel Diffusion Model (DM) based approach for semi-supervised medical image segmentation

Training

• training the backbone

python train.py --dataset CXR14 --data_dir <PATH TO CXR14 DATASET>

• our method

python train.py --experiment TEDM --data_dir <PATH TO JSRT DATASET> --n_labelled_images <TRAINING SET SIZE>

• LEDM method

python train.py --experiment LEDM --data_dir <PATH TO JSRT DATASET> --n_labelled_images <TRAINING SET SIZE>

• LEDMe method

python train.py --experiment LEDMe --data_dir <PATH TO JSRT DATASET> --n_labelled_images <TRAINING SET SIZE>

• baseline method

python train.py --experiment JSRT_baseline --data_dir <PATH TO JSRT DATASET> --n_labelled_images <TRAINING SET SIZE>

Testing

• update

  • DATADIR in paths dataloaders/JSRT.py, dataloaders/NIH.py and dataloaders/Montgomery.py
  • NIHPATH, NIHFILE, MONPATH and MONFILE in paths auxiliary/postprocessing/run_tests.py and auxiliary/postprocessing/testing_shared_weights.py

• for baseline and LEDM methods, run

python auxiliary/postprocessing/run_tests.py --experiment <PATH TO LOG FOLDER>

• for our method, run

python auxiliary/postprocessing/testing_shared_weights.py --experiment <PATH TO LOG FOLDER>

Figures and reporting

VS Code notebooks can be found in auxiliary/notebooks_and_reporting.