Vision-Based Approach for Autism Diagnosis Using Transfer Learning and Eye-Tracking
- 1. HEALTHINF 2022 Vision-BasedApproach forAutism DiagnosisUsing TransferLearning and Eye-Tracking Mahmoud Elbattah, Jean-Luc Guérin, Romuald Carette, Federica Cilia, Gilles Dequen Laboratoire MIS Université de Picardie Jules Verne (UPJV), France mahmoud.elbattah@u-picardie.fr https://www.researchgate.net/publication/358842561_Vision-based_Approach_for_Autism_Diagnosis_using_Transfer_Learning_and_Eye-tracking
- 2. HEALTHINF 2022 Background:Autism SpectrumDisorder • Autism Spectrum Disorder (ASD) is a pervasive developmental disorder characterised by a set of impairments including social communication problems.1 • ASD has been considered to affect about 1% of the world’s population (US Dep. of Health, 2018). 2 • The hallmark of autism is an impairment of the ability to make and maintain eye contact. 3 2 1 L. Wing, and J. Gould, “Severe Impairments of Social Interaction and AssociatedAbnormalitiesin Children: Epidemiologyand Classification”.Journal of Autism and DevelopmentalDisorders,9(1), pp.11-29,1979. 2 U.S. Departmentof Health & Human Services.Data and statistics | autism spectrum disorder(asd) | ncbddd | cdc, 2018.URL: https://www.cdc.gov/ncbddd/autism/data.html. 3 Coonrod,E. E. and Stone, W. L. (2004).Early concerns of parents of children with autistic and nonautistic disorders.Infants & Young Children, 17(3),258–268.
- 3. HEALTHINF 2022 Background:Eye-TrackingTechnology 3 Image Source: https://imotions.com/blog/eye-tracking/ J.H. Goldberg, and J.I. Helfman, “Visual scanpath representation”, In Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications, ACM, 2010, pp. 203-210. Scan-path
- 4. HEALTHINF 2022 Motivation Our Goal: • Detecting ASD-diagnosed individual in eye-tracking data. Key Idea: • Compactly render eye movements into an image-based format while maintaining the dynamic characteristics of eye motion. • As such, the classification task could be approached as image classification. 4
- 5. HEALTHINF 2022 Our EarlierWork (HEALTHINF 2019) 5 Carette, R., Elbattah, M., Cilia, F.,Dequen, G., Guérin, J, & Bosche, J. (2019). Learning to predict autism spectrumdisorder based on the visualpatterns of eye-tracking scanpaths. In Proceedingsof the 12th InternationalConference on Health Informatics(HEALTHINF).
- 6. HEALTHINF 2022 Data Description 6 Number of Participants(ASD, TD) 59 (29, 30) Gender Distribution (M, F) 38 (≈ 64%), 21 (≈ 36%) Age (Mean, Median) years 7.88, 8.1 CARS Score (Mean, Median) 32.97, 34.50
- 7. HEALTHINF 2022 Image DatasetDescription • 547 images: 328 (Non-ASD), 219 (ASD) • Image dimensions: 640x480 7 ASD Non-ASD Carette, R., Elbattah, M., Dequen, G., Guérin, J. L., & Cilia, F. (2018, September). Visualization of eye-tracking patterns in autism spectrum disorder: method and dataset. In 2018 Thirteenth International Conference on Digital Information Management (ICDIM) (pp. 248-253). IEEE.
- 8. HEALTHINF 2022 TransferLearning Models(FeatureExtraction) • VGG-16: A deep CNN architecture developed by a group of researchers from the University of Oxford (Simonyan, and Zisserman, 2014). • ResNet: Residual Networks (ResNet) (He et al., 2016). • DenseNet: Densely Connected Convolutional Network (DenseNet) architecture (Huang et al., 2017). 8
- 9. HEALTHINF 2022 DatasetSplitting(3-FoldCross-Validation) The dataset was split using the following stepwise procedures: 1. Split Participants: Initially, the group of 59 participants was randomly split into two independent sets (i.e. train and test). 2. Match Images: Based on the IDs of participants, the images were matched and loaded into the train and test sets. 3. Repeat: Step #1 and Step #2 would be repeated for each round of the cross-validation process. 9
- 11. HEALTHINF 2022 ExperimentalResults(cont’d) 11 AVERAGE PRECISION-RECALL OF MODELS. Model Recall (~) Precision (~) VGG-16 0.56 0.67 ResNet 0.54 0.65 DenseNet 0.55 0.65
- 12. HEALTHINF 2022 Conclusionsand Limitations • Popular vision models such as VGG-16, ResNet, and DenseNet could achieve a quite promising performance. • The TL approach was largely applicable, even though the source dataset (i.e., ImageNet) is assumed to have included quite different types of images. • It is not claimed that the TL approach could provide superior performance. • However, it is conceived that the scarcity or imbalance of datasets could make such TL approaches attractive for further investigation. 12