This Repo contains code for paper Attention-based CNN for KL Grade Classification: Data from the Osteoarthritis Initiative
./data
contains data for training/testing data for detector and classifier.OAI_summary.csv
file is from OAI dataset, and contains metadata for all patients. The train/test split gave the performance mentioned in paper../model_weights
contains model weights that can be readily used bytorch.load
with the performance mentioned in paper../oai-knee-detection
contains code to train a detector and generate all annotations./oai-xray-klg
contains code to train classifier and generate the attention map from GradCAM
Please refer to requirements.txt
for install all dependencies for this project. ./data
folder contains example content file for train/test data used in dataloader for both detector and classifier. ./model_weights
folder contains model weights that achieved the performance metrics mentioned in paper.
This repo consists of two parts. To reproduce the entire experiments, you will need to
- Train a detector and use the detector to annotate all OAI dataset, and generate train/test data for the classifier. See documentation in
./oai-knee-detection
. - Train and test the classifier by following documentation in
./oai-xray-klg
@inproceedings{zhang2020attention,
title={Attention-based cnn for kl grade classification: Data from the osteoarthritis initiative},
author={Zhang, Bofei and Tan, Jimin and Cho, Kyunghyun and Chang, Gregory and Deniz, Cem M},
booktitle={2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
pages={731--735},
year={2020},
organization={IEEE}
}