Identifying Visual Attention Features Accurately Discerning Between Autism and Typically Developing: a Deep Learning Framework-猫眼课题宝

DOI：

10.1007/s12539-022-00510-6

发表时间：

2022-04

期刊：

Interdisciplinary Sciences: Computational Life Sciences

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

J. Xie;Longfei Wang;Paula J Webster;Yang Yao;Jiayao Sun;Shuo Wang;Huihui Zhou

非典型视觉注意是自闭症谱系障碍（ASD）的一个标志。识别注意力特征，准确区分ASD患者和个体水平的典型发展（TD）患者仍然是一个挑战。在这项研究中，我们开发了一个新的系统框架，结合了高精度的深度学习分类，深度学习分割，图像消融和分类能力的直接测量，以识别自闭症识别的判别特征。我们的双流模型达到了最先进的性能，分类精度为0.95。使用这个框架，两个新的类别的功能，食品和饮料和户外物体，被确定为歧视性的注意功能，除了以前报道的功能，包括中心对象和人脸等。改变注意的新类别有助于了解相关的非典型行为在ASD。重要的是，基于本研究中确定的组合前9个特征的曲线下面积（AUC）为0.92，允许在个体水平上进行准确分类。我们还获得了一个小的，但信息丰富的数据集的12个图像的AUC为0.86，这表明一个潜在的有效的方法，为ASD的临床诊断。总之，我们基于VGG-16的深度学习框架提供了一种新颖而强大的工具来识别和理解ASD中的异常视觉注意力，这反过来将有助于识别ASD的生物标志物。图形摘要

Atypical visual attention is a hallmark of autism spectrum disorder (ASD). Identifying the attention features accurately discerning between people with ASD and typically developing (TD) at the individual level remains a challenge. In this study, we developed a new systematic framework combining high accuracy deep learning classification, deep learning segmentation, image ablation and a direct measurement of classification ability to identify the discriminative features for autism identification. Our two-stream model achieved the state-of-the-art performance with a classification accuracy of 0.95. Using this framework, two new categories of features, Food & drink and Outdoor-objects, were identified as discriminative attention features, in addition to the previously reported features including Center-object and Human-faces, etc. Altered attention to the new categories helps to understand related atypical behaviors in ASD. Importantly, the area under curve (AUC) based on the combined top-9 features identified in this study was 0.92, allowing an accurate classification at the individual level. We also obtained a small but informative dataset of 12 images with an AUC of 0.86, suggesting a potentially efficient approach for the clinical diagnosis of ASD. Together, our deep learning framework based on VGG-16 provides a novel and powerful tool to recognize and understand abnormal visual attention in ASD, which will, in turn, facilitate the identification of biomarkers for ASD.Graphical abstract

Identifying Visual Attention Features Accurately Discerning Between Autism and Typically Developing: a Deep Learning Framework

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