[MICCAI 2023] This is the official repository of our paper Combat Long-tails in Medical Classification with Relation-aware Consistency and Virtual Features Compensation.
isic-cli (https://github.com/ImageMarkup/isic-cli)
torch==1.12.0
torchvision==0.13.0
wandb==0.13.5
torchsampler==0.1.2
scikit-image==0.19.3
imbalanced-learn==0.9.0
albumentations==1.3.0
scikit-learn==1.0.2
To start with, download the official ISIC datasets and split them into train/val/test:
# ISIC 2019
bash ./prepare_datasets/ISIC2019LT/download_ISIC2019.sh
# ISIC Archive
bash ./prepare_datasets/ISIC_Archive/download_isic_archive.sh
python ./prepare_datasets/ISIC_Archive/merge.py
The first-stage training with the MRC module. The model weights will be saved to './checkpoints'.
python stage1.py
- If you are going to use the wandb to log the training process, please replace wandb.login(key="[Your wandb key here]") with your own key.
The second-stage training with the VFC module.
python stage2.py
@inproceedings{pan2023combat,
title={Combat Long-Tails in Medical Classification with Relation-Aware Consistency and Virtual Features Compensation},
author={Pan, Li and Zhang, Yupei and Yang, Qiushi and Li, Tan and Chen, Zhen},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={14--23},
year={2023},
organization={Springer}
}