Brain tumors are one of the life-threatening forms of cancer. Previous studies have classified brain tumors using deep neural networks. In this paper, we perform the later task using a collaborative deep learning technique, more specifically split learning. Split learning allows collaborative learning via neural networks splitting into two (or more) parts, a client-side network and a server-side network. The client-side is trained to a certain layer called the cut layer. Then, the rest of the training is resumed on the server-side network. Vertical distribution, a method for distributing data among organizations, was implemented where several hospitals hold different attributes of information for the same set of patients. To the best of our knowledge this paper will be the first paper to implement both split learning and vertical distribution for brain tumor classification. Using both techniques, we were able to achieve train and test accuracy greater than 90\% and 70\%, respectively.

翻译：脑肿瘤是威胁生命的癌症形式之一。先前的研究已使用深度神经网络对脑肿瘤进行分类。本文采用协作深度学习技术，具体而言是拆分学习，来完成这一任务。拆分学习通过将神经网络拆分为两个（或多个）部分（客户端网络和服务器端网络）实现协作学习。客户端网络训练至某一指定层（称为切割层），之后剩余训练在服务器端网络上继续。我们实现了垂直分布——一种在机构间分配数据的方法——其中多家医院对同一组患者持有不同的属性信息。据我们所知，本文是首次同时采用拆分学习与垂直分布进行脑肿瘤分类的研究。通过结合这两种技术，我们实现了训练准确率超过90%和测试准确率超过70%的结果。