论文标题
比较用于检测阿尔茨海默氏病的卷积神经网络训练参数和对可视化的影响
Comparison of Convolutional neural network training parameters for detecting Alzheimers disease and effect on visualization
论文作者
论文摘要
卷积神经网络（CNN）已成为检测图像数据中模式的强大工具。最近的论文报告了使用脑MRI数据在疾病检测领域有希望的结果。尽管到目前为止，从CNN模型中获得了高度准确性，但几乎没有任何论文提供有关驱动此精度的功能或图像区域的信息，因为缺少或具有挑战性的适用方法。最近，工具箱Innvestigate已获得，实施了各种最先进的方法来进行深度学习可视化。当前，对可视化算法进行比较的需求很大，以概述这些算法的实际实用性和能力。 因此，本论文有两个目标：1。系统地评估CNN超参数对模型准确性的影响。 2。要比较质量的各种可视化方法（即随机性/焦点，声音）。
Convolutional neural networks (CNN) have become a powerful tool for detecting patterns in image data. Recent papers report promising results in the domain of disease detection using brain MRI data. Despite the high accuracy obtained from CNN models for MRI data so far, almost no papers provided information on the features or image regions driving this accuracy as adequate methods were missing or challenging to apply. Recently, the toolbox iNNvestigate has become available, implementing various state of the art methods for deep learning visualizations. Currently, there is a great demand for a comparison of visualization algorithms to provide an overview of the practical usefulness and capability of these algorithms. Therefore, this thesis has two goals: 1. To systematically evaluate the influence of CNN hyper-parameters on model accuracy. 2. To compare various visualization methods with respect to the quality (i.e. randomness/focus, soundness).
