Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Neural Netw. 2020 Jun;126:218-234. doi: 10.1016/j.neunet.2020.03.017. Epub 2020 Mar 28.
Convolutional neural network (CNN) models have recently demonstrated impressive performance in medical image analysis. However, there is no clear understanding of why they perform so well, or what they have learned. In this paper, a three-dimensional convolutional neural network (3D-CNN) is employed to classify brain MRI scans into two predefined groups. In addition, a genetic algorithm based brain masking (GABM) method is proposed as a visualization technique that provides new insights into the function of the 3D-CNN. The proposed GABM method consists of two main steps. In the first step, a set of brain MRI scans is used to train the 3D-CNN. In the second step, a genetic algorithm (GA) is applied to discover knowledgeable brain regions in the MRI scans. The knowledgeable regions are those areas of the brain which the 3D-CNN has mostly used to extract important and discriminative features from them. For applying GA on the brain MRI scans, a new chromosome encoding approach is proposed. The proposed framework has been evaluated using ADNI (including 140 subjects for Alzheimer's disease classification) and ABIDE (including 1000 subjects for Autism classification) brain MRI datasets. Experimental results show a 5-fold classification accuracy of 0.85 for the ADNI dataset and 0.70 for the ABIDE dataset. The proposed GABM method has extracted 6 to 65 knowledgeable brain regions in ADNI dataset (and 15 to 75 knowledgeable brain regions in ABIDE dataset). These regions are interpreted as the segments of the brain which are mostly used by the 3D-CNN to extract features for brain disease classification. Experimental results show that besides the model interpretability, the proposed GABM method has increased final performance of the classification model in some cases with respect to model parameters.
卷积神经网络（CNN）模型在医学图像分析中最近取得了令人瞩目的成绩。然而，我们并不清楚它们为什么表现得如此出色，或者它们学到了什么。在本文中，我们使用三维卷积神经网络（3D-CNN）将脑 MRI 扫描分类为两个预定义的组别。此外，我们还提出了一种基于遗传算法的脑掩蔽（GABM）方法作为一种可视化技术，为 3D-CNN 的功能提供了新的见解。所提出的 GABM 方法包括两个主要步骤。在第一步中，使用一组脑 MRI 扫描来训练 3D-CNN。在第二步中，应用遗传算法（GA）来发现 MRI 扫描中具有知识的区域。具有知识的区域是指 3D-CNN 主要用于从中提取重要和有区别的特征的大脑区域。为了在脑 MRI 扫描上应用 GA，我们提出了一种新的染色体编码方法。所提出的框架已使用 ADNI（包括 140 名阿尔茨海默病分类的受试者）和 ABIDE（包括 1000 名自闭症分类的受试者）脑 MRI 数据集进行了评估。实验结果表明，ADNI 数据集的 5 倍分类准确率为 0.85，ABIDE 数据集的为 0.70。所提出的 GABM 方法在 ADNI 数据集（和 ABIDE 数据集）中提取了 6 到 65 个具有知识的脑区（15 到 75 个具有知识的脑区）。这些区域被解释为大脑的片段，3D-CNN 主要使用这些片段来提取特征进行脑疾病分类。实验结果表明，除了模型可解释性之外，在所提出的 GABM 方法在某些情况下，还可以通过模型参数增加分类模型的最终性能。
