Learning to Predict where the Children with Asd Look

Duan, Huiyu; Zhai, Guangtao; Min, Xiongkuo; Fang, Yi; Che, Zhaohui; Yang, Xiaokang; Zhi, Cheng; Yang, Hua; Liu, Ning

doi:10.1109/icip.2018.8451338

Cited by 34 publications

(20 citation statements)

References 12 publications

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“…Nebout et al [75] designed a coarse-to-fine convolutional neural network (CNN) to predict saliency maps for ASD children that provides better results than 6 of existing saliency models. This study reported that no center bias is applicable for the visual attention of individuals with ASD, which contradicts the findings of other studies in [26,116]. Dris et al [25] proposed a method of classifying ASD using fixation duration on different regions of interest in the image and got 88.6% specificity using an SVM classifier.…”

Section: Analyzing Gaze Patterncontrasting

confidence: 65%

Data-Driven Automated Detection of Autism Spectrum Disorder Using Activity Analysis: A Review

Shahid¹,

Rahman²,

Ahmed³

et al. 2020

Preprint

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Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that limits social interactions, cognitive skills, and abilities. Since ASD can last during an affected person's entire life cycle, the diagnosis at the early onset can yield a significant positive impact. The current medical diagnostic systems (e.g., DSM-5/ICD-10) are somewhat subjective; rely purely on the behavioral observation of symptoms, and hence, some individuals often go misdiagnosed or late-diagnosed. Therefore, researchers have focused on developing data-driven automated diagnosis systems with less screening time, low cost, and improved accuracy while significantly reducing professional intervention. Human Activity Analysis (HAA) is considered one of the most promising niches in computer vision research. This paper aims to analyze its potentialities in the automated detection of autism by tracking the exclusive characteristics of autistic individuals such as repetitive behavior, atypical walking style, and unusual visual saliency. This review provides a detailed inspection of HAA-based autism detection literature published in 2011 on-wards depicting core approaches, challenges, probable solutions, available resources, and scopes of future exploration in this arena. According to our study, deep learning outperforms machine learning in ASD detection with a classification accuracy of 76\% to 95\% on different datasets comprise of video, image, or skeleton data that recorded participants performing a large number of actions. However, machine learning provides satisfactory results on datasets with a small number of action classes and has a range of 60\% to 93\% accuracy among numerous studies. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

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Section: Analyzing Gaze Patterncontrasting

confidence: 65%

Data-Driven Automated Detection of Autism Spectrum Disorder Using Activity Analysis: A Review

Shahid¹,

Rahman²,

Ahmed³

et al. 2020

Preprint

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“…A very popular fixation point dataset containing 2000 images from 20 different categories has been proposed by [95]. Some research groups focused their efforts on gathering eye-movement data from cohort of people affected by some cognitive disorders such as ASD (Authism Spectrum Disorder) [96], [97], [98].…”

Section: B Eye-tracking Datasetsmentioning

confidence: 99%

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

et al. 2020

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In the first seconds of observation of an image, several visual attention processes are involved in the identification of the visual targets that pop-out from the scene to our eyes. Saliency is the quality that makes certain regions of an image stand out from the visual field and grab our attention. Saliency detection models, inspired by visual cortex mechanisms, employ both colour and luminance features. Furthermore, both locations of pixels and presence of objects influence the Visual Attention processes. In this paper, we propose a new saliency method based on the combination of the distribution of interest points in the image with multiscale analysis, a centre bias module and a machine learning approach. We use perceptually uniform colour spaces to study how colour impacts on the extraction of saliency. To investigate eye-movements and assess the performances of saliency methods over object-based images, we conduct experimental sessions on our dataset ETTO (Eye Tracking Through Objects). Experiments show our approach to be accurate in the detection of saliency concerning state-of-the-art methods and accessible eye-movement datasets. The performances over object-based images are excellent and consistent on generic pictures. Besides, our work reveals interesting findings on some relationships between saliency and perceptually uniform colour spaces.

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“…As shown in [19], by finetuning five state-of-the-art saliency prediction models on the ASD eye-tracking dataset, it was found that SalGAN [18] and SAM-VGG [12] outperform the other three models including SALICON [9], ML-Net [10] and SAM-ResNet [12]. Therefore, SalGAN and SAM-VGG are selected for comparison in this paper.…”

Section: Comparison With State-of-the-art Modelsmentioning

confidence: 99%

Saliency Prediction via Multi-Level Features and Deep Supervision for Children with Autism Spectrum Disorder

Wei

Huang

et al. 2019

2019 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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This paper proposes a novel saliency prediction model for children with autism spectrum disorder (ASD). Based on the convolutional neural network, the multi-level features are extracted and integrated to three attention maps, which are used to generate the predicted saliency map. The deep supervision on the attention maps is exploited to build connections between ground truths and the deep layers in the neural network during training. Furthermore, by performing the single-side clipping operation on the ground truths, our model is encouraged to enhance the capacity of better predicting the most salient regions in images. Experimental results on an ASD eye-tracking dataset demonstrate that our model achieves the better saliency prediction performance for children with ASD.

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Learning to Predict where the Children with Asd Look

Cited by 34 publications

References 12 publications

Data-Driven Automated Detection of Autism Spectrum Disorder Using Activity Analysis: A Review

Data-Driven Automated Detection of Autism Spectrum Disorder Using Activity Analysis: A Review

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

Saliency Prediction via Multi-Level Features and Deep Supervision for Children with Autism Spectrum Disorder

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