Multi-limb Split Learning for Tumor Classification on Vertically Distributed Data

TL;DR

This paper introduces a novel approach combining split learning and vertical data distribution to classify brain tumors across multiple hospitals, achieving high accuracy while preserving data privacy.

Contribution

It is the first to implement both split learning and vertical distribution techniques for brain tumor classification, demonstrating their effectiveness.

Findings

Train accuracy over 90%

Test accuracy over 70%

First implementation of combined methods for this task

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.