Sharing medical datasets between hospitals is challenging because of the privacy-protection problem and the massive cost of transmitting and storing many high-resolution medical images. However, dataset distillation can synthesize a small dataset such that models trained on it achieve comparable performance with the original large dataset, which shows potential for solving the existing medical sharing problems. Hence, this paper proposes a novel dataset distillation-based method for medical dataset sharing. Experimental results on a COVID-19 chest X-ray image dataset show that our method can achieve high detection performance even using scarce anonymized chest X-ray images.
@article{arxiv.2209.14603,
title = {Dataset Distillation for Medical Dataset Sharing},
author = {Guang Li and Ren Togo and Takahiro Ogawa and Miki Haseyama},
journal= {arXiv preprint arXiv:2209.14603},
year = {2022}
}
Comments
Accepted by AAAI-23 Workshop on Representation Learning for Responsible Human-Centric AI