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.
3. 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.

4. For testing, just load the experiment which its model you need.

5. The rest of the files:

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