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Background and objective: Sharing of medical data is required to enable thecross-agency flow of healthcare information and construct high-accuracycomputer-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 toinefficient medical data sharing. Therefore, this study proposes a novelsoft-label dataset distillation method for medical data sharing.  Methods: The proposed method distills valid information of medical image dataand generates several compressed images with different data distributions foranonymous medical data sharing. Furthermore, our method can extract essentialweights of DCNN models to reduce the memory required to save trained models forefficient medical data sharing.  Results: The proposed method can compress tens of thousands of images intoseveral soft-label images and reduce the size of a trained model to a fewhundredths of its original size. The compressed images obtained afterdistillation have been visually anonymized; therefore, they do not contain theprivate information of the patients. Furthermore, we can realize high-detectionperformance with a small number of compressed images.  Conclusions: The experimental results show that the proposed method canimprove the efficiency and security of medical data sharing.

Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing