标题： GL-ICNN：阿尔茨海默病诊断和预测的端到端可解释卷积神经网络 显示英文标题

标题： GL-ICNN: An End-To-End Interpretable Convolutional Neural Network for the Diagnosis and Prediction of Alzheimer's Disease

摘要： 基于卷积神经网络（CNN）的深度学习方法在利用影像数据改善阿尔茨海默病（AD）痴呆的早期和准确诊断方面显示出巨大潜力。 然而，这些方法尚未在临床实践中得到广泛应用，这可能是因为深度学习模型的可解释性有限。 可解释提升机（EBM）是一种透明模型，但无法直接从输入影像数据中学习特征。 在本研究中，我们提出了一种新颖的可解释模型，该模型结合了CNN和EBMs用于AD的诊断和预测。 我们开发了一种创新的训练策略，交替训练CNN组件作为特征提取器和EBM组件作为输出模块，以形成端到端模型。 该模型以影像数据作为输入，并提供预测结果和可解释的特征重要性度量。 我们在阿尔茨海默病神经影像学倡议（ADNI）数据集以及Health-RI Parelsnoer神经退行性疾病生物银行（PND）作为外部测试集验证了所提出的模型。 所提出的模型在AD和对照组分类中的曲线下面积（AUC）达到了0.956，在ADNI队列中对轻度认知障碍（MCI）向AD转化的预测AUC达到了0.694。 所提出的模型是一种透明模型，其性能与其他最先进的黑盒模型相当。 我们的代码可在以下位置公开获取：https://anonymous.4open.science/r/GL-ICNN. 显示英文摘要

摘要： Deep learning methods based on Convolutional Neural Networks (CNNs) have shown great potential to improve early and accurate diagnosis of Alzheimer's disease (AD) dementia based on imaging data. However, these methods have yet to be widely adopted in clinical practice, possibly due to the limited interpretability of deep learning models. The Explainable Boosting Machine (EBM) is a glass-box model but cannot learn features directly from input imaging data. In this study, we propose a novel interpretable model that combines CNNs and EBMs for the diagnosis and prediction of AD. We develop an innovative training strategy that alternatingly trains the CNN component as a feature extractor and the EBM component as the output block to form an end-to-end model. The model takes imaging data as input and provides both predictions and interpretable feature importance measures. We validated the proposed model on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and the Health-RI Parelsnoer Neurodegenerative Diseases Biobank (PND) as an external testing set. The proposed model achieved an area-under-the-curve (AUC) of 0.956 for AD and control classification, and 0.694 for the prediction of conversion of mild cognitive impairment (MCI) to AD on the ADNI cohort. The proposed model is a glass-box model that achieves a comparable performance with other state-of-the-art black-box models. Our code is publicly available at: https://anonymous.4open.science/r/GL-ICNN.