PIPNet3D：MRI扫描中阿尔茨海默病的可解释检测
PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans
AI总结：
PIPNet3D是一种基于部分原型神经网络的可解释模型，用于从结构MRI中诊断阿尔茨海默病，在保持与黑盒模型相同准确率的同时，其推理与医学知识高度一致。
摘要：
来自神经影像检查的信息越来越多地被用于支持痴呆症（如阿尔茨海默病）的诊断。虽然当前的临床实践主要基于视觉检查和特征工程，但深度学习方法可用于自动化分析并发现新的影像生物标志物。部分原型神经网络（PP-NN）是标准黑盒模型的一种替代方案，并在通用计算机视觉中显示出有前景的结果。PP-NN的推理基于完全无监督学习得到的原型图像区域，并结合一个易于理解的决策层。我们提出了PIPNet3D，一种用于体积图像的PP-NN。我们将PIPNet3D应用于从结构磁共振成像（sMRI）进行阿尔茨海默病的临床诊断。我们在一个系统评估框架下评估原型的质量，提出了新的功能接地指标来评估大脑原型，并开发了一种评估方案来评估其与领域专家的一致性。我们的结果表明，PIPNet3D是一个可解释、紧凑的阿尔茨海默病诊断模型，其推理与医学领域知识高度一致。值得注意的是，PIPNet3D达到了与其黑盒对应模型相同的准确率；并且从其决策过程中移除临床上不相关的原型并不会降低预测性能。
英文摘要：
Information from neuroimaging examinations 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 diagnosis of Alzheimer's Disease from structural Magnetic Resonance Imaging (sMRI). We assess the quality of prototypes under a systematic evaluation framework, propose new functionally grounded metrics to evaluate brain prototypes and develop an evaluation scheme to assess their coherency 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.
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