Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.
Cereb Cortex
; 34(13): 72-83, 2024 May 02.
Article
en En
| MEDLINE
| ID: mdl-38696605
ABSTRACT
Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Encéfalo
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Diagnóstico Precoz
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Trastorno del Espectro Autista
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Aprendizaje Profundo
Límite:
Child, preschool
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Female
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Humans
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Infant
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Male
Idioma:
En
Revista:
Cereb Cortex
Asunto de la revista:
CEREBRO
Año:
2024
Tipo del documento:
Article
País de afiliación:
China