Yin Zhuowen, Ding Xinyao, Zhang Xin, Wu Zhengwang, Wang Li, Xu Xiangmin, Li Gang
School of Electronics and Information Engineering, South China University of Technology, 510641 Guangzhou, Guangdong Province, China.
Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States.
Cereb Cortex. 2024 May 2;34(13):72-83. doi: 10.1093/cercor/bhae069.
Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.
自闭症谱系障碍已成为日益严重的公共卫生威胁。自闭症谱系障碍的早期诊断对于及时、有效的干预和治疗至关重要。然而，基于沟通和行为模式的传统诊断方法对于2岁以下儿童并不可靠。鉴于自闭症谱系障碍婴儿存在神经发育异常的证据，我们采用一种基于深度学习的新方法，从本质上稀缺、类别不平衡且异质的结构磁共振图像中提取关键特征，用于自闭症的早期诊断。具体而言，我们提出了一个连体验证框架来扩充稀缺数据，以及一个无监督压缩器，通过提取关键特征来缓解数据不平衡。我们还提出了权重约束，通过在验证期间给不同样本赋予不同的投票权重来应对样本异质性，并使用路径签名从纵向的两个时间点数据中揭示有意义的发育特征。我们进一步提取了专注于机器学习的脑区用于自闭症诊断。大量实验表明，我们的方法在实际场景中表现良好，超越了现有的机器学习方法，并为自闭症早期诊断提供了解剖学见解。
