Abstract
People with autism spectrum disorder (ASD) show distinguishing preferences for specific visual stimuli compared to typically developed (TD) individuals, opening the door for objective and quantitative screening by eye-tracking data analysis. However, existing eye-tracking-based ASD screening approaches often assume that there are no individual differences and that all stimuli contribute equally to the prediction of an ASD. Consequently, a fixed number of images are usually selected by a pre-defined strategy for further training and testing, ignoring the distinct characteristics of various subjects viewing the same image. To address the aforementioned difficulties, we propose a novel Uncertainty-inspired ASD Screening Network (UASN) that dynamically modifies the contribution of each stimulus viewed by different subjects. Specifically, we estimate the uncertainty of each stimulus by considering the variation between the subject’s fixation map and the ones of the two clinical groups (i.e., ASD and TD) and further utilize it for weighting the training loss. Besides, to reduce the diagnosis time, instead of the shuffle-appeared mode of image viewing, we propose an uncertainty-based personalized diagnosis method to dynamically rank the viewing images according to the preferences of different subjects, which can achieve high prediction accuracy with only a small set of images. Experiments demonstrate the superior performance of our proposed UASN.
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References
Chang, J., Lan, Z., Cheng, C., Wei, Y.: Data uncertainty learning in face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020
Chen, S., Zhao, Q.: Attention-based autism spectrum disorder screening with privileged modality. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1181–1190 (2019)
Corbetta, M., Shulman, G.L.: Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3(3), 201–215 (2002)
Duan, H., et al.: A dataset of eye movements for the children with autism spectrum disorder. In: Proceedings of the 10th ACM Multimedia Systems Conference, pp. 255–260 (2019)
El Ghaoui, L., Lanckriet, G.R.G., Natsoulis, G., et al.: Robust classification with interval data (2003)
Frazier, T.W., et al.: A meta-analysis of gaze differences to social and nonsocial information between individuals with and without autism. J. Am. Acad. Child Adolesc. Psychiatry 56(7), 546–555 (2017)
Freeth, M., Chapman, P., Ropar, D., Mitchell, P.: Do gaze cues in complex scenes capture and direct the attention of high functioning adolescents with ASD? Evidence from eye-tracking. J. Autism Dev. Disord. 40(5), 534–547 (2010)
Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Jiang, M., Zhao, Q.: Learning visual attention to identify people with autism spectrum disorder. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3267–3276 (2017)
Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2106–2113 (2009). https://doi.org/10.1109/ICCV.2009.5459462
Klin, A., Lin, D.J., Gorrindo, P., Ramsay, G., Jones, W.: Two-year-olds with autism orient to non-social contingencies rather than biological motion. Nature 459(7244), 257–261 (2009)
Li, B., Sharma, A., Meng, J., Purushwalkam, S., Gowen, E.: Applying machine learning to identify autistic adults using imitation: an exploratory study. PLoS ONE 12(8), e0182652 (2017)
Liu, W., Li, M., Yi, L.: Identifying children with autism spectrum disorder based on their face processing abnormality: a machine learning framework. Autism Res. 9(8), 888–898 (2016)
Maenner, M.J., et al.: Prevalence and characteristics of autism spectrum disorder among children aged 8 years-autism and developmental disabilities monitoring network, 11 sites, United States, 2018. MMWR Surveill. Summ. 70(11), 1 (2021)
McPartland, J.C., Webb, S.J., Keehn, B., Dawson, G.: Patterns of visual attention to faces and objects in autism spectrum disorder. J. Autism Dev. Disord. 41(2), 148–157 (2011)
Pelphrey, K.A., Sasson, N.J., Reznick, J.S., Paul, G., Goldman, B.D., Piven, J.: Visual scanning of faces in autism. J. Autism Dev. Disord. 32(4), 249–261 (2002)
Pierce, K., Marinero, S., Hazin, R., McKenna, B., Barnes, C.C., Malige, A.: Eye tracking reveals abnormal visual preference for geometric images as an early biomarker of an autism spectrum disorder subtype associated with increased symptom severity. Biol. Psychiat. 79(8), 657–666 (2016)
Sasson, N.J., Turner-Brown, L.M., Holtzclaw, T.N., Lam, K.S., Bodfish, J.W.: Children with autism demonstrate circumscribed attention during passive viewing of complex social and nonsocial picture arrays. Autism Res. 1(1), 31–42 (2008)
Thabtah, F.: Autism spectrum disorder screening: machine learning adaptation and DSM-5 fulfillment. In: Proceedings of the 1st International Conference on Medical and Health Informatics 2017, pp. 1–6 (2017)
Wang, S., et al.: Atypical visual saliency in autism spectrum disorder quantified through model-based eye tracking. Neuron 88(3), 604–616 (2015)
Wang, Z., Li, Y., Guo, Y., Fang, L., Wang, S.: Data-uncertainty guided multi-phase learning for semi-supervised object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4568–4577 (2021)
Xu, Y., et al.: Data uncertainty in face recognition. IEEE Trans. Cybern. 44(10), 1950–1961 (2014)
Acknowledgements
This work was supported by the Beijing Natural Science Foundation (M22022, L211015) and the National Natural Science Foundation of China (62271042, 61906013).
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Zhang, Y., Huang, Y., Qi, J., Zhang, S., Tian, M., Tian, Y. (2023). Uncertainty Inspired Autism Spectrum Disorder Screening. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14224. Springer, Cham. https://doi.org/10.1007/978-3-031-43904-9_39
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