by Lie Ju, Yicheng Wu, Lin Wang, Zhen Yu, Xin Zhao, Xin Wang, Paul Bonnington and Zongyuan Ge*
This is an official Pytorch implementation of our paper published in MICCAI 2022.
Flexible Sampling for Long-tailed Skin Lesion Classification
The code is avaliable now!
We have also provided other widely-used tricks for long-tailed learning. Feel free to use them!
Please follow:
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On the 8-class dataset, please download ISIC 2019 dataset.
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Then you can use our provided split training, validation, and test data, all stored in a NumPy format. Please use 'np.load()' to extract the information.
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For the 14-class dataset, please use official API to download the extra images from the ISIC dataset gallery using our provided .csv file.
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Also, the split data is provided. Please use 'np.load()' to extract the information.
If you find this repository is helpful for your work, please cite our work:
@inproceedings{ju2022flexible,
title={Flexible Sampling for Long-Tailed Skin Lesion Classification},
author={Ju, Lie and Wu, Yicheng and Wang, Lin and Yu, Zhen and Zhao, Xin and Wang, Xin and Bonnington, Paul and Ge, Zongyuan},
booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2022: 25th International Conference, Singapore, September 18--22, 2022, Proceedings, Part III},
pages={462--471},
year={2022},
organization={Springer}
}
[1] Cui, Yin, et al. "Class-balanced loss based on effective number of samples." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.
[2] Lin, Tsung-Yi, et al. "Focal loss for dense object detection." Proceedings of the IEEE international conference on computer vision. 2017.
[3] Zhang, Songyang, et al. "Distribution alignment: A unified framework for long-tail visual recognition." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021.
[4] Cao, Kaidi, et al. "Learning imbalanced datasets with label-distribution-aware margin loss." Advances in neural information processing systems 32 (2019).
[5] Zhang, Hongyi, et al. "mixup: Beyond empirical risk minimization." arXiv preprint arXiv:1710.09412 (2017).