期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 227, 期 -, 页码 -出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2022.107189
关键词
Medical image distillation; Medical data sharing; Model compression; Anonymization
类别
资金
- AMED [JP21zf0127004]
- Hokkaido University-Hitachi Collaborative Education and Research Support Program
- MEXT Doctoral program for Data-Related InnoVation Expert Hokkaido University (D-DRIVE-HU) program
The study proposed a novel soft-label dataset distillation method for medical data sharing, which can compress images, improve the efficiency of DCNN models, and achieve efficient and secure medical data sharing.
Background and objective: Sharing of medical data is required to enable the cross-agency flow of health-care 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 in-formation 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. (c) 2022 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据