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

Omar Ads, Mayar Alfares, Mohammed Salem

Proceedings of the Tenth International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 88-92, 2021.


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 specific layer named as 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 is 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.

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BibTeX

@inproceedings{ads21_icicis, author = {Ads, Omar and Alfares, Mayar and Salem, Mohammed}, title = {Multi-limb Split Learning for Tumor Classification on Vertically Distributed Data}, booktitle = {Proceedings of the Tenth International Conference on Intelligent Computing and Information Systems (ICICIS)}, year = {2021}, pages = {88-92} }