A novel explainable neural network for Alzheimer’s disease diagnosis

Abstract

Visual classification for medical images has been dominated by convolutional neural networks (CNNs) for years. Though they have shown great performance on accuracy, some of them provide decisions that are hard to explain while others encode information from irrelevant or noisy regions. In this work, we try to close this gap by proposing an explainable framework which consists of a predictor and an explainable tool, so as to provide accurate diagnoses with intuitive visualization maps and prediction basis. Specifically, the predictor is designed by applying attention mechanisms to multi-scale features so as to learn and discover class discriminative latent representations that are close to each brain volume’s label. Meanwhile, to explain our predictor, we propose the novel explainable tool which includes a high-resolution visualization method and a prediction-basis creation and retrieval module. The former effectively integrates the feature maps of intermediate layers as well as the last convolutional layer, which surpasses state-of-the-art visualization approaches in producing high-resolution representations with more accurate localization of discriminative areas. While the latter provides prediction basis evidence via retrieved volumes with similar latent representations which are accessible to neurologists. Extensive experiments show that the proposed framework achieves higher level of accuracy and explainability over other state-of-the-art solutions. More importantly, it localizes crucial brain areas with clearer boundaries, less noises, which matches background knowledge in the neuroscience literature.