论文标题
3D卷积神经网络中的学习形状特征和抽象用于检测阿尔茨海默氏病
Learning Shape Features and Abstractions in 3D Convolutional Neural Networks for Detecting Alzheimer's Disease
论文作者
论文摘要
深层神经网络 - 尤其是卷积神经网络（Convnet）已成为图像分类，模式识别和各种计算机视觉任务的最新技术。 Convnet在医疗领域具有巨大的潜力，可以分析医学数据以有效地诊断疾病。基于Convnet模型从MRI数据提取的特征，早期诊断对于预防进步和治疗阿尔茨海默氏病至关重要。尽管具有出色表现的能力，但模型决定的不可解释性可能会导致误诊，这可能会威胁生命。在本论文中，使用各种可视化技术研究了3D Convnet的学到的形状特征和抽象来检测阿尔茨海默氏病。还检查了网络结构，使用过滤器尺寸和过滤器形状的变化如何影响模型的整体性能和学习的功能。不同模型的LRP相关性图揭示了大脑哪些部分与分类决策更相关。通过激活最大化比较学习的过滤器，显示了如何在网络的不同层中编码模式。最后，实施了从卷积自动编码器中转移学习，以检查增加输入片的训练样本数量是否增加了提取低级功能的训练样本，从而提高了学习的功能和模型性能。
Deep Neural Networks - especially Convolutional Neural Network (ConvNet) has become the state-of-the-art for image classification, pattern recognition and various computer vision tasks. ConvNet has a huge potential in medical domain for analyzing medical data to diagnose diseases in an efficient way. Based on extracted features by ConvNet model from MRI data, early diagnosis is very crucial for preventing progress and treating the Alzheimer's disease. Despite having the ability to deliver great performance, absence of interpretability of the model's decision can lead to misdiagnosis which can be life threatening. In this thesis, learned shape features and abstractions by 3D ConvNets for detecting Alzheimer's disease were investigated using various visualization techniques. How changes in network structures, used filters sizes and filters shapes affects the overall performance and learned features of the model were also inspected. LRP relevance map of different models revealed which parts of the brain were more relevant for the classification decision. Comparing the learned filters by Activation Maximization showed how patterns were encoded in different layers of the network. Finally, transfer learning from a convolutional autoencoder was implemented to check whether increasing the number of training samples with patches of input to extract the low-level features improves learned features and the model performance.
