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Autism Spectrum Disorder01:19

Autism Spectrum Disorder

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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Modeling in Therapy01:26

Modeling in Therapy

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
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Updated: Jun 29, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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早期自闭症诊断的创新策略:积极学习和领域适应优化优化.

Mohammad Shafiul Alam1, Elfatih A A Elsheikh2, F M Suliman2

  • 1Department of Mechatronics Engineering, International Islamic University Malaysia, Jln Gombak, Kuala Lumpur 53100, Malaysia.

Diagnostics (Basel, Switzerland)
|March 27, 2024
PubMed
概括

积极学习通过调整模型以适应各种面部图像数据集来改善早期自闭症谱系障碍 (ASD) 诊断. 基于不确定性的方法提高了各种数据源的准确性,减少了注释需求.

关键词:
在ASD中,使用的是ASD.积极学习是积极学习.深度学习是一种深度学习.域名适应 域名适应面部图像 面部图像 面部图像

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科学领域:

  • 计算机科学 计算机科学
  • 医疗成像医学成像
  • 发展心理学 发展心理学

背景情况:

  • 自闭症谱系障碍 (ASD) 的早期诊断至关重要,但由于面部图像数据集的域变异而受到阻碍.
  • 当前的模型在应用于各种数据源时,与性能退化作斗争.

研究的目的:

  • 调查主动学习的有效性,特别是基于不确定性的采样,用于早期ASD诊断中的域适应.
  • 提高深度学习模型在不同面部图像数据集中的性能,以检测ASD.

主要方法:

  • 在Kaggle ASD和YTUIA数据集中分析域变异.
  • 使用Xception和ResNet50V2卷积神经网络进行转移学习的评估.
  • 基于不确定性的积极学习的应用,以适应领域.

主要成果:

  • 预先训练的模型在单个数据集上获得了高精度 (95-96%).
  • 结合数据集导致性能下降.
  • 基于不确定性的积极学习减轻了准确度的下降,在目标数据集上实现了80% (Xception) 和79% (ResNet50V2) 的准确性.

结论:

  • 基于不确定性的积极学习是有效的领域适应在早期的ASD诊断.
  • 这种方法提高了模型的准确性,并减少了注释要求.
  • 这些发现支持开发更强大的ASD检测工具,用于各种数据集.