Code for CheXaid: Deep Learning Assistance for Physician Diagnosis of Tuberculosis using Chest X-Rays in Patients with HIV.
This is a deep learning model developed by Stanford Machine Learning Group (stanfordmlgroup.github.io) to help physicians detect Tuberculosis (TB) among patients with HIV infection using their Chest X-Rays.
In order to train the model, the train data directory and other hyperparameters can be set in util/hypersearch.py. By default, the training data directory is set to be dummy/, which has 128 randomly generated images. The util/hypersearch.py script includes information about the pretrained model to use for transfer learning (checkpoint paths, number of original classes), data args (task sequence, number of the fold to train and validate on for k-fold cross validation, image transformations), model args (model to be used, list of covariates, loss function), optimizer args and logger args. More details about these can be found in args/.
After properly setting them, the training script can be run as:
python train.py
Alternatively, hyperparameters and file settings can be set by adding arguments to the training command:
python train.py \
--batch_size=64 \
--eval_pulm=True \
--num_epochs=10
In order to test a model that has been trained as above, the test script can be run as:
python test.py \
--eval_pulm=True \
--transform_classifier=False \
--save_cams=True \
--task_sequence=pulm \
--ckpt_path=ckpts/new_gt_1549918027265_4CF892/iter_6656_pulm-valid_pulm_tbAUROC_0.79.pth.tar \
--split=test
Note that split=True is necessary to run the script on the test data, and save_cams=True makes sure that the class activation maps of the model on the test data are saved.