Böhle Moritz, Eitel Fabian, Weygandt Martin, Ritter Kerstin
Berlin Institute of Health, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.
Department of Psychiatry and Psychotherapy, Bernstein Center for Computational Neuroscience, Berlin, Germany.
Front Aging Neurosci. 2019 Jul 31;11:194. doi: 10.3389/fnagi.2019.00194. eCollection 2019.
Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer's disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent, making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance/relevance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation ("Which change in voxels would change the outcome most?"), the LRP method is able to directly highlight positive contributions to the network classification in the input space. In particular, we show that (1) the LRP method is very specific for individuals ("Why does this person have AD?") with high inter-patient variability, (2) there is very little relevance for AD in healthy controls and (3) areas that exhibit a lot of relevance correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed relevance per brain area, e.g., relevance density or relevance gain. Although these metrics produce very individual "fingerprints" of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity toward the underlying model and computation parameters, we conclude that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data.
深度神经网络在许多医学成像任务中取得了最先进的成果，包括基于结构磁共振成像（MRI）数据的阿尔茨海默病（AD）检测。然而，网络决策通常被认为是高度不透明的，这使得这些算法难以应用于临床常规。在本研究中，我们建议使用逐层相关性传播（LRP）来可视化基于MRI数据的AD卷积神经网络决策。与其他可视化方法类似，LRP在输入空间中生成一个热图，指示每个体素对最终分类结果的重要性/相关性。与引导反向传播产生的敏感性图（“体素的哪些变化会对结果产生最大影响？”）不同，LRP方法能够直接突出输入空间中对网络分类的积极贡献。特别是，我们表明：（1）LRP方法对个体（“为什么这个人患有AD？”）非常具有特异性，患者间变异性高；（2）在健康对照中与AD的相关性非常小；（3）显示出高度相关性的区域与文献中已知的情况相关性良好。为了量化后者，我们计算每个脑区相关性总和的大小校正指标，例如相关性密度或相关性增益。尽管这些指标为AD患者产生了非常个性化的相关性模式“指纹”，但颞叶包括海马体在内的区域被赋予了很大的重要性。在讨论了几个局限性，如对基础模型和计算参数的敏感性之后，我们得出结论，LRP在协助临床医生解释基于结构MRI数据诊断AD（以及潜在的其他疾病）的神经网络决策方面可能具有很高的潜力。
