Abstract
Autism spectrum disorder (ASD) is one of the most common neurodevelopmental disorders, which impairs the communication and interaction ability of patients. Intensive intervention in early ASD can effectively improve symptoms, so the diagnosis of ASD children receives significant attention. However, clinical assessment relies on experienced diagnosticians, which makes the diagnosis of ASD children difficult to popularize, especially in remote areas. In this paper, we propose a simple yet effective pipeline to diagnose ASD children, which comprises a convenient and fast strategy of video acquisition and an advanced deep learning framework. In our framework, firstly, we extract sufficient head-related features from the collected videos by a generic toolbox. Secondly, we propose a head-related characteristic (HRC) attention mechanism to select the most discriminative disease-related features adaptively. Finally, a convolutional neural network is used to diagnose ASD children by exploring the temporal information from the selected features. We also build a video dataset based on our strategy of video acquisition that contains 82 children to verify the effectiveness of the proposed pipeline. Experiments on this dataset show that our deep learning framework achieves a superior performance of ASD children diagnosis. The code and dataset will be available at https://github.com/xiaotaiyangcmm/DASD.
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Acknowledgement
This work was supported in part by the National Key R &D Program of China under Grant 2017YFA0700800, the National Natural Science Foundation of China under Grant 62021001, and the University Synergy Innovation Program of Anhui Province No. GXXT-2019-025.
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Cai, M., Li, M., Xiong, Z., Zhao, P., Li, E., Tang, J. (2022). An Advanced Deep Learning Framework for Video-Based Diagnosis of ASD. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_42
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