Uncertainty Inspired Early Autism Spectrum Disorder Screening via Contrastive Image-Viewing Paradigm
Eye-tracking technology is found effective in revealing the specific visual preference of Autism Spectrum Disorder (ASD) which can be characterized by high systemizing and low empathizing abilities. Early diagnosis is vital for ASD’s subsequent treatment. However, existing eye-tracking-ba...
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10804194/ |
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| author | Ying Zhang Yaping Huang Jiansong Qi Sihui Zhang Mei Tian Yi Tian Fanchao Meng Lin Guan Tianyi Chang |
| author_facet | Ying Zhang Yaping Huang Jiansong Qi Sihui Zhang Mei Tian Yi Tian Fanchao Meng Lin Guan Tianyi Chang |
| author_sort | Ying Zhang |
| collection | DOAJ |
| description | Eye-tracking technology is found effective in revealing the specific visual preference of Autism Spectrum Disorder (ASD) which can be characterized by high systemizing and low empathizing abilities. Early diagnosis is vital for ASD’s subsequent treatment. However, existing eye-tracking-based methods suffer from long diagnostic times and low diagnostic accuracy due to the lack of awareness of gaze preference derived from individual differences. Moreover, there is only one publicly available eye-tracking dataset that employs a simple image free-viewing paradigm to collect the gaze patterns of ASD and typically developed (TD) subjects with an average age of 8 years, thus can not effectively support the early diagnosis for preschool children. To tackle the difficulties, in this paper, we first propose an Uncertainty-inspired ASD Screening Network (UASN) that dynamically estimates the contribution of each stimulus viewed by different subjects, and secondly, we design a contrastive image-viewing paradigm and further collect eye movement data from preschool children to reveal the visual behaviors of ASD children accordingly. Specifically, in UASN, we estimate the uncertainty of each stimulus and use it for more efficient model training and a more simplified personalized diagnosis procedure. Besides, by synthesizing two images with the opposite semantic representations and recruiting ASD and TD subjects aged 2-6, we construct a new CI4ASD dataset, which offers a novel contrastive image-viewing paradigm for better diagnosis of ASD in children. Comprehensive experiments are conducted and results have evidenced the effectiveness of the proposed UASN and eye-tracking paradigm. |
| format | Article |
| id | doaj-art-6805563c9ad04e63868755593b47fc1a |
| institution | Kabale University |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-6805563c9ad04e63868755593b47fc1a2025-01-16T00:00:09ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-013312213210.1109/TNSRE.2024.351869310804194Uncertainty Inspired Early Autism Spectrum Disorder Screening via Contrastive Image-Viewing ParadigmYing Zhang0https://orcid.org/0009-0002-8872-4443Yaping Huang1https://orcid.org/0000-0002-4465-372XJiansong Qi2Sihui Zhang3https://orcid.org/0009-0004-4780-8362Mei Tian4https://orcid.org/0000-0002-7388-3954Yi Tian5https://orcid.org/0000-0001-6054-7970Fanchao Meng6Lin Guan7Tianyi Chang8Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, ChinaBeijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, ChinaBeijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, ChinaBeijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, ChinaBeijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, ChinaBeijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, ChinaBeijing Anding Hospital, Capital Medical University, Beijing, ChinaBeijing Anding Hospital, Capital Medical University, Beijing, ChinaBeijing Anding Hospital, Capital Medical University, Beijing, ChinaEye-tracking technology is found effective in revealing the specific visual preference of Autism Spectrum Disorder (ASD) which can be characterized by high systemizing and low empathizing abilities. Early diagnosis is vital for ASD’s subsequent treatment. However, existing eye-tracking-based methods suffer from long diagnostic times and low diagnostic accuracy due to the lack of awareness of gaze preference derived from individual differences. Moreover, there is only one publicly available eye-tracking dataset that employs a simple image free-viewing paradigm to collect the gaze patterns of ASD and typically developed (TD) subjects with an average age of 8 years, thus can not effectively support the early diagnosis for preschool children. To tackle the difficulties, in this paper, we first propose an Uncertainty-inspired ASD Screening Network (UASN) that dynamically estimates the contribution of each stimulus viewed by different subjects, and secondly, we design a contrastive image-viewing paradigm and further collect eye movement data from preschool children to reveal the visual behaviors of ASD children accordingly. Specifically, in UASN, we estimate the uncertainty of each stimulus and use it for more efficient model training and a more simplified personalized diagnosis procedure. Besides, by synthesizing two images with the opposite semantic representations and recruiting ASD and TD subjects aged 2-6, we construct a new CI4ASD dataset, which offers a novel contrastive image-viewing paradigm for better diagnosis of ASD in children. Comprehensive experiments are conducted and results have evidenced the effectiveness of the proposed UASN and eye-tracking paradigm.https://ieeexplore.ieee.org/document/10804194/Autism spectrum disorder screeningeye-tracking paradigmdeep learning |
| spellingShingle | Ying Zhang Yaping Huang Jiansong Qi Sihui Zhang Mei Tian Yi Tian Fanchao Meng Lin Guan Tianyi Chang Uncertainty Inspired Early Autism Spectrum Disorder Screening via Contrastive Image-Viewing Paradigm IEEE Transactions on Neural Systems and Rehabilitation Engineering Autism spectrum disorder screening eye-tracking paradigm deep learning |
| title | Uncertainty Inspired Early Autism Spectrum Disorder Screening via Contrastive Image-Viewing Paradigm |
| title_full | Uncertainty Inspired Early Autism Spectrum Disorder Screening via Contrastive Image-Viewing Paradigm |
| title_fullStr | Uncertainty Inspired Early Autism Spectrum Disorder Screening via Contrastive Image-Viewing Paradigm |
| title_full_unstemmed | Uncertainty Inspired Early Autism Spectrum Disorder Screening via Contrastive Image-Viewing Paradigm |
| title_short | Uncertainty Inspired Early Autism Spectrum Disorder Screening via Contrastive Image-Viewing Paradigm |
| title_sort | uncertainty inspired early autism spectrum disorder screening via contrastive image viewing paradigm |
| topic | Autism spectrum disorder screening eye-tracking paradigm deep learning |
| url | https://ieeexplore.ieee.org/document/10804194/ |
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