Dong Qunxi, Zhang Jie, Li Qingyang, Thompson Pau M, Caselli Richard J, Ye Jieping, Wang Yalin
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
Hum Brain Artif Intell (2019). 2019;1072:21-35. doi: 10.1007/978-981-15-1398-5_2. Epub 2019 Nov 10.
Computer-aided diagnosis (CAD) systems for medical images are seen as effective tools to improve the efficiency of diagnosis and prognosis of Alzheimers disease (AD). The current state-of-the-art models for many images analyzing tasks are based on Convolutional Neural Networks (CNN). However, the lack of training data is a common challenge in applying CNN to the diagnosis of AD and its prodromal stages. Another challenge for CAD applications is the controversy between the requiring of longitudinal cortical structural information for higher diagnosis/prognosis accuracy and the computing ability for processing varied imaging features. To address these two challenges, we propose a novel computer-aided AD diagnosis system CNN-Multitask Stochastic Coordinate Coding (MSCC) which integrates CNN with transfer learning strategy, a novel MSCC algorithm and our effective AD-related biomarkers-multivariate morphometry statistics (MMS). We applied the novel CNN-MSCC system on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset to predict future cognitive clinical measures with baseline Hippocampal/Ventricle MMS features and cortical thickness. The experimental results showed that CNN-MSCC achieved superior results. The proposed system may aid in expediting the diagnosis of AD progress, facilitating earlier clinical intervention, and resulting in improved clinical outcomes.
用于医学图像的计算机辅助诊断（CAD）系统被视为提高阿尔茨海默病（AD）诊断和预后效率的有效工具。当前许多图像分析任务的最先进模型都是基于卷积神经网络（CNN）。然而，缺乏训练数据是将CNN应用于AD及其前驱阶段诊断时的一个常见挑战。CAD应用的另一个挑战是，为了获得更高的诊断/预后准确性而需要纵向皮质结构信息与处理各种成像特征的计算能力之间存在争议。为了解决这两个挑战，我们提出了一种新颖的计算机辅助AD诊断系统CNN-多任务随机坐标编码（MSCC），该系统将CNN与迁移学习策略、一种新颖的MSCC算法以及我们有效的AD相关生物标志物——多变量形态测量统计（MMS）相结合。我们将新颖的CNN-MSCC系统应用于阿尔茨海默病神经影像倡议（ADNI）数据集，以利用基线海马体/脑室MMS特征和皮质厚度预测未来的认知临床指标。实验结果表明，CNN-MSCC取得了优异的结果。所提出的系统可能有助于加快AD进展的诊断，促进早期临床干预，并改善临床结果。
