Multi-task Semi-supervised Learning for Lobe Segmentation

Introduction for several directories with their specific functions:

• data (training datasets are saved here)
• futils (common used functions and models including building models, compute metrics)
• logs (save monitor metrics during training)
• models (save trained models)
• results (save the training/validation/testing results including Dice, MSD, Hausdorff distance, false positive, etfc.)

Introduction for each files

• Use python train_ori_fit_rec_epoch.py to train model.
• Use write_preds_save_dice.py to evaluate the trained model.
• Modify set_parameters.py to set custom parameters.
• Scipts files script* are used to submit job to HPC cluster.
• plot_curve* are used to plot training loss curve.