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In recent days, Convolutional Neural Networks (CNN) have demonstratedimpressive performance in medical image analysis. However, there is a lack ofclear understanding of why and how the Convolutional Neural Network performs sowell for image analysis task. How CNN analyzes an image and discriminates amongsamples of different classes are usually considered as non-transparent. As aresult, it becomes difficult to apply CNN based approaches in clinicalprocedures and automated disease diagnosis systems. In this paper, we considerthis issue and work on visualizing and understanding the decision ofConvolutional Neural Network for Alzheimer's Disease (AD) Diagnosis. We developa 3D deep convolutional neural network for AD diagnosis using brain PET scansand propose using five visualizations techniques - Sensitivity Analysis(Backpropagation), Guided Backpropagation, Occlusion, Brain Area Occlusion, andLayer-wise Relevance Propagation (LRP) to understand the decision of the CNN byhighlighting the relevant areas in the PET data.

Understanding 3D CNN Behavior for Alzheimer's Disease Diagnosis from Brain PET Scan