Related papers: Dataset Distillation for Medical Dataset Sharing

Dataset Distillation for Medical Dataset Sharing

URL: http://arxiv.org/abs/2209.14603v2
Date: Fri, 30 Sep 2022 03:50:35 GMT
Title: Dataset Distillation for Medical Dataset Sharing
Authors: Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama
Abstract summary: dataset distillation can synthesize a small dataset such that models trained on it achieve comparable performance with the original large dataset. 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.
Score: 38.65823547986758
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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