March 22, 2024, 4:46 a.m. | Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

cs.CV updates on arXiv.org arxiv.org

arXiv:2104.02857v2 Announce Type: replace-cross
Abstract: 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 …

abstract accuracy anonymous arxiv cad computer construct cs.cv data dataset data sharing diagnosis distillation eess.iv gradient however image medical medical data paper privacy protection ray systems type x-ray

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