MT-UNet

This repo contains python codes used for "Multi-task UNet: Jointly Boosting Saliency Prediction and Disease Classification on Chest X-ray Images", Hongzhi Zhu et al., submitted to Medical Imaging with Deep learning (MIDL) 2022.

• Folder Data contain files used for data pre-processing (before training).
• Folder Module contain files used for network training and evaluation.

The following packages are used with Python 3.7:

• PyTorch 1.8.0
• torchvision 0.9.0
• tensorboard 2.3.0
• pandas 1.1.0
• matplotlib 3.3.1
• numpy 1.19.2
• scipy 1.5.2
• sklearn 0.23.2
• opencv 4.0.1
• pydicom 2.2.0

Data folder

To run the code, simply execute data.py through “python data.py” in command line or with other Python IDEs. After execution, new folders containing per-processed and split datasets ready for training will be created in the execution directory. The raw dataset is partitioned into training (70%), validation (10%) and testing (20%) subsets. Seeding (value 0) is used for reproducibility.

Module folder

To run the code, simply execute RUN.py through “python RUN.py” in command line or with other Python IDEs. After execution, a new folder run will be created (if not already exists) in the execution directory. For each execution of RUN.py, a new folder with a random name will be created inside folder run for the temporary storage of network parameters as well as recoding training details and evaluation results. The following lists the file details inside the folder:

• params_MTL_UNet_preset_.txt, records parameters and data related to network training.
• params_NetLearn_.txt, records hyper-parameters for the network.
• QuickHelper_summary.txt, records other data during execution.
• NET_XXXX/classification_report.json, records classification evaluation metrics, and XXXXX are random characters generated during run time.
• NET_XXXX/classification_results.csv, records raw classification output for each test image.
• NET_XXXX/prediction_report.json, records classification and saliency prediction performance matrices.
• NET_XXXX/NET.pt, stores the parameters for the best performing network during or after training.
• NET_XXXX/training_process.png, visualizes the change of learning rate, losses, and validation metrics during the training process. During execution, RUN.py will also print training details and evaluation results to the console.