Compressed gastric image generation based on soft-label dataset distillation for medical data sharing.

Journal: Computer methods and programs in biomedicine
Published Date: Dec 1, 2022

Abstract

BACKGROUND AND OBJECTIVE:   Sharing of medical data is required to enable the cross-agency flow of healthcare information and construct high-accuracy computer-aided diagnosis systems. However, the large sizes of medical datasets, the massive amount of memory of saved deep convolutional neural network (DCNN) models, and patients privacy protection are problems that can lead to inefficient medical data sharing. Therefore, this study proposes a novel soft-label dataset distillation method for medical data sharing.

Authors

  • Guang Li
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
  • Ren Togo
    Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan.
  • Takahiro Ogawa
    Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan.
  • Miki Haseyama
    Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan.

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