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Multi‐task diagnosis for autism spectrum disorders using multi‐modality features: A multi‐center study
Human brain mapping, 2017-06, Vol.38 (6), p.3081-3097
Wang, Jun
Wang, Qian
Peng, Jialin
Nie, Dong
Zhao, Feng
Kim, Minjeong
Zhang, Han
Wee, Chong‐Yaw
Wang, Shitong
Shen, Dinggang
2017
Details
Autor(en) / Beteiligte
Wang, Jun
Wang, Qian
Peng, Jialin
Nie, Dong
Zhao, Feng
Kim, Minjeong
Zhang, Han
Wee, Chong‐Yaw
Wang, Shitong
Shen, Dinggang
Titel
Multi‐task diagnosis for autism spectrum disorders using multi‐modality features: A multi‐center study
Ist Teil von
Human brain mapping, 2017-06, Vol.38 (6), p.3081-3097
Ort / Verlag
United States: John Wiley & Sons, Inc
Erscheinungsjahr
2017
Link zum Volltext
Quelle
Wiley HSS Collection
Beschreibungen/Notizen
Autism spectrum disorder (ASD) is a neurodevelopment disease characterized by impairment of social interaction, language, behavior, and cognitive functions. Up to now, many imaging‐based methods for ASD diagnosis have been developed. For example, one may extract abundant features from multi‐modality images and then derive a discriminant function to map the selected features toward the disease label. A lot of recent works, however, are limited to single imaging centers. To this end, we propose a novel multi‐modality multi‐center classification (M3CC) method for ASD diagnosis. We treat the classification of each imaging center as one task. By introducing the task‐task and modality‐modality regularizations, we solve the classification for all imaging centers simultaneously. Meanwhile, the optimal feature selection and the modeling of the discriminant functions can be jointly conducted for highly accurate diagnosis. Besides, we also present an efficient iterative optimization solution to our formulated problem and further investigate its convergence. Our comprehensive experiments on the ABIDE database show that our proposed method can significantly improve the performance of ASD diagnosis, compared to the existing methods. Hum Brain Mapp 38:3081–3097, 2017. © 2017 Wiley Periodicals, Inc.
Sprache
Englisch
Identifikatoren
ISSN: 1065-9471
eISSN: 1097-0193
DOI: 10.1002/hbm.23575
Titel-ID: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5427005
Format
–
Schlagworte
Adolescent
,
Algorithms
,
Autism
,
Autism Spectrum Disorder - classification
,
Autism Spectrum Disorder - diagnostic imaging
,
autism spectrum disorders
,
Brain
,
Child
,
Classification
,
Cognitive ability
,
Diagnosis
,
Discriminant Analysis
,
Disease control
,
Disorders
,
Feature extraction
,
feature selection
,
Female
,
Humans
,
Image Processing, Computer-Assisted - methods
,
Imaging
,
Iterative methods
,
Magnetic Resonance Imaging - methods
,
Male
,
modality‐modality relation
,
multitask learning
,
multi‐modality data
,
Neurodevelopmental disorders
,
Neuroimaging
,
Optimization
,
Pattern Recognition, Automated
,
Reproducibility of Results
,
Social behavior
,
task‐task relation
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