Interactive Visualization of Deep Learning for 3D Brain Data Analysis

Abstract

With multiple hidden layers and massive combinations of features and weights, deep learning models are hard to understand, and even more difficult to interact with. In this paper we describe a visual analytics platform to help with the understanding of and interaction with the deep learning process of human brain image data. A brain connectome network dataset is used to train a classifier for the diagnosis of Alzheimer's Disease (AD). 3D rendering of brain images is integrated into the interactive visualization process of a deep neural network to bring contextual information of the application to the analysis framework. A backpropagation algorithm is applied to track the image features that are captured by each node in the hidden layers. Our results demonstrate that interactive visualization can not only help the understanding of the deep learning process, but also provide a platform for domain experts to interact with and assist in the learning process, which can potentially enhance the interpretability and accuracy of the analysis.