Overview

• This is a PyTorch implementation of the paper Interpretable and Lightweight 3-D Deep Learning Model For Automated ACL Diagnosis by Jeon et al.

• Paper DOI: 10.1109/JBHI.2021.3081355

Prerequisites

The software is developed in Python 3.7+. For the deep learning, the PyTorch 1.3.1+ framework is used.

Code structure

1. Everything can be ran from ./main_ACL.py.
2. The data preprocessing parameters, hyper-parameters, model parameters, and directories can be modified from ./config/config.yaml.

• Also, you should first choose an experiment name (if you are starting a new experiment) for training, in which all the evaluation and loss value statistics, tensorboard events, and model & checkpoints will be stored. Furthermore, a config.yaml file will be created for each experiment storing all the information needed.
• For testing, just load the experiment which its model you need.

3. The rest of the files:

• ./models/ directory contains all the model architectures.
• ./Train_Valid_ACL.py contains the training and validation processes.