  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 tiny distilled dataset that
has a different data distribution. Different from model distillation, our
method aims to find the optimal distilled images, distilled labels and the
optimized learning rate. Experimental results show that the proposed method can
not only effectively compress the medical dataset but also anonymize medical
images to protect the patient's private information. The proposed approach can
improve the efficiency and security of medical data sharing.
