Abstract
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.
Abstract (translated)
脑瘤是致命癌症之一,以往的研究使用深度神经网络对脑瘤进行分类。在本文中,我们使用协作深度学习方法,更具体地说是分裂学习。分裂学习通过将神经网络分裂成两个(或更多)部分来实现协作学习。客户端网络被训练到称为切割层的某个层。然后,服务器端网络的其余部分被恢复。垂直分布是一种将数据分布在组织之间的方法,在几个医院中对同一组患者有不同的信息属性采取了这种方法。据我们所知,本文将成为第一个实现分裂学习和垂直分布用于脑瘤分类的论文。通过两种方法,我们能够实现训练和测试精度超过90%和70%。
URL
https://arxiv.org/abs/2301.11468
