A novel explainable neural network for Alzheimer’s disease diagnosis
Pattern Recognition(2022)
摘要
•We propose a novel 3D interpretable model, dubbed MAXNet, which can effectively aggregate multi-scale features for Alzheimer’s disease detection and learn latent features that are representative to each volume’s label.•We present a novel high-resolution visualization method, termed Highresolution Activation Mapping (HAM), that produces high-resolution visual explanations for the precise localization of disease areas through aggregating the attentional representations from multi-resolution responses in parallel.•We propose a Prediction-basis Creation and Retrieval (PCR) module, which leverages latent representations to collect similar reference samples as visual evidence for the case analysis of Alzheimer’s disease.
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关键词
Explainable neural networks,XAI,High-resolution heatmap,MRI
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