  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.
  Methods: The proposed method distills valid information of medical image data
and generates several compressed images with different data distributions for
anonymous medical data sharing. Furthermore, our method can extract essential
weights of DCNN models to reduce the memory required to save trained models for
efficient medical data sharing.
  Results: The proposed method can compress tens of thousands of images into
several soft-label images and reduce the size of a trained model to a few
hundredths of its original size. The compressed images obtained after
distillation have been visually anonymized; therefore, they do not contain the
private information of the patients. Furthermore, we can realize high-detection
performance with a small number of compressed images.
  Conclusions: The experimental results show that the proposed method can
improve the efficiency and security of medical data sharing.
