This paper presents a soft-label anonymous gastric X-ray image distillation method based on a gradient descent approach. The sharing of medical data is demanded to construct high-accuracy computer-aided diagnosis (CAD) systems. However, the large size of the medical dataset and privacy protection are remaining problems in medical data sharing, which hindered the research of CAD systems. The idea of our distillation method is to extract the valid information of the medical dataset and generate a tiny distilled dataset that has a different data distribution. Different from model distillation, our method aims to find the optimal distilled images, distilled labels and the optimized learning rate. Experimental results show that the proposed method can not only effectively compress the medical dataset but also anonymize medical images to protect the patient's private information. The proposed approach can improve the efficiency and security of medical data sharing.
@article{arxiv.2104.02857,
title = {Soft-Label Anonymous Gastric X-ray Image Distillation},
author = {Guang Li and Ren Togo and Takahiro Ogawa and Miki Haseyama},
journal= {arXiv preprint arXiv:2104.02857},
year = {2024}
}
Comments
The first paper to explore real-world dataset distillation; Work was done in 2019 and published as a conference paper at ICIP 2020