ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data
ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data
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DOI：
10.3389/fninf.2019.00070
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发表时间：
2019-11-27
影响因子：
3.5
通讯作者：
Saeed, Fahad
中科院分区：
文献类型：
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作者：
Eslami, Taban;Mirjalili, Vahid;Saeed, Fahad
众所周知，自闭症谱系障碍 (ASD) 等异质性精神障碍很难诊断，尤其是儿童。目前的精神科诊断过程纯粹基于症状学的行为观察（DSM-5/ICD-10），可能容易误诊。为了推动该领域更加定量的诊断，我们需要先进且可扩展的机器学习基础设施，使我们能够识别精神健康疾病的可靠生物标志物。在本文中，我们提出了一个名为 ASD-DiagNet 的框架，用于仅使用 fMRI 数据将 ASD 受试者与健康受试者进行分类。我们使用自动编码器和单层感知器 (SLP) 设计并实现了联合学习过程，从而提高了提取特征的质量和模型的优化参数。此外，我们设计并实现了基于可用特征向量的线性插值的数据增强策略，该策略使我们能够生成机器学习模型训练所需的合成数据集。该方法在自闭症脑成像数据交换中心提供的公共数据集上进行了评估，其中包括来自 17 个不同脑成像中心的 1, 035 名受试者。我们的机器学习模型优于来自 10 个成像中心的其他最先进的方法，分类准确度提高了 28%，最大准确度达到 82%。本文介绍的机器学习技术除了能产生更好的质量外，在执行时间方面也具有巨大的优势（其他方法为 40 分钟，而其他方法为 7 小时）。实现的代码可作为 GPL 许可证在我们实验室的 GitHub 门户 (https://github.com/pcdslab/ASD-DiagNet) 上获取。
Heterogeneous mental disorders such as Autism Spectrum Disorder (ASD) are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioral observation of symptomology (DSM-5/ICD-10) and may be prone to misdiagnosis. In order to move the field toward more quantitative diagnosis, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders. In this paper, we propose a framework called ASD-DiagNet for classifying subjects with ASD from healthy subjects by using only fMRI data. We designed and implemented a joint learning procedure using an autoencoder and a single layer perceptron (SLP) which results in improved quality of extracted features and optimized parameters for the model. Further, we designed and implemented a data augmentation strategy, based on linear interpolation on available feature vectors, that allows us to produce synthetic datasets needed for training of machine learning models. The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1, 035 subjects coming from 17 different brain imaging centers. Our machine learning model outperforms other state of the art methods from 10 imaging centers with increase in classification accuracy up to 28% with maximum accuracy of 82%. The machine learning technique presented in this paper, in addition to yielding better quality, gives enormous advantages in terms of execution time (40 min vs. 7 h on other methods). The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/ASD-DiagNet).