论文标题
在COVID-19，X射线和胆固醇数据集中，在数据不平衡限制下，多站点拆分学习对隐私医疗系统的可行性研究
Feasibility Study of Multi-Site Split Learning for Privacy-Preserving Medical Systems under Data Imbalance Constraints in COVID-19, X-Ray, and Cholesterol Dataset
论文作者
论文摘要
似乎越来越多的人参加了在线上载内容，数据和信息的竞赛；医院也没有忽略这一趋势。现在，医院在多站点医疗数据共享方面处于最前沿，以共享健康记录和诊断患者的方式提供开创性的进步。在现代医学研究中共享医疗数据至关重要。但是，与所有数据共享技术一样，挑战是平衡改进的治疗方法和保护患者的个人信息。本文提供了一种新颖的拆分学习算法，创造了“多站点拆分学习”一词，该术语可以在多家医院之间安全地转移医疗数据，而不必担心会揭露患者记录中包含的个人数据。它还探讨了改变终端系统数量以及数据不平衡对深度学习绩效比率的影响。凭经验得出了确保患者数据的私密性，同时赋予了最佳的分裂学习配置指南。我们认为，使用COVID-19患者的CT扫描，X射线骨扫描和胆固醇水平的医学数据，我们认为我们多站点分裂学习算法的好处，尤其是关于保留隐私因素的好处。
It seems as though progressively more people are in the race to upload content, data, and information online; and hospitals haven't neglected this trend either. Hospitals are now at the forefront for multi-site medical data sharing to provide groundbreaking advancements in the way health records are shared and patients are diagnosed. Sharing of medical data is essential in modern medical research. Yet, as with all data sharing technology, the challenge is to balance improved treatment with protecting patient's personal information. This paper provides a novel split learning algorithm coined the term, "multi-site split learning", which enables a secure transfer of medical data between multiple hospitals without fear of exposing personal data contained in patient records. It also explores the effects of varying the number of end-systems and the ratio of data-imbalance on the deep learning performance. A guideline for the most optimal configuration of split learning that ensures privacy of patient data whilst achieving performance is empirically given. We argue the benefits of our multi-site split learning algorithm, especially regarding the privacy preserving factor, using CT scans of COVID-19 patients, X-ray bone scans, and cholesterol level medical data.
