Information from neuroimaging examinations (CT, MRI) is increasingly used to
support diagnoses of dementia, e.g., Alzheimer's disease. While current
clinical practice is mainly based on visual inspection and feature engineering,
Deep Learning approaches can be used to automate the analysis and to discover
new image-biomarkers. Part-prototype neural networks (PP-NN) are an alternative
to standard blackbox models, and have shown promising results in general
computer vision. PP-NN's base their reasoning on prototypical image regions
that are learned fully unsupervised, and combined with a simple-to-understand
decision layer. We present PIPNet3D, a PP-NN for volumetric images. We apply
PIPNet3D to the clinical case study of Alzheimer's Disease diagnosis from
structural Magnetic Resonance Imaging (sMRI). We assess the quality of
prototypes under a systematic evaluation framework, propose new metrics to
evaluate brain prototypes and perform an evaluation with domain experts. Our
results show that PIPNet3D is an interpretable, compact model for Alzheimer's
diagnosis with its reasoning well aligned to medical domain knowledge. Notably,
PIPNet3D achieves the same accuracy as its blackbox counterpart; and removing
the remaining clinically irrelevant prototypes from its decision process does
not decrease predictive performance.

使用神经影像学（CT，MRI）数据以支持痴呆症的诊断，特别是阿尔茨海默病。通过使用深度学习方法自动分析并发现新的图像生物标记，我们提出了一种基于部分原型神经网络（PP-NN）的模型 PIPNet3D，并应用于阿尔茨海默病的临床研究。结果表明，PIPNet3D 是一种可解释的、紧凑的阿尔茨海默病诊断模型，其推理过程与医学领域知识相吻合，且具有与其黑盒模型相同的准确度。