ArticleIEEE transactions on cybernetics2019
Sparse Multiview Task-Centralized Ensemble Learning for ASD Diagnosis Based on Age- and Sex-Related Functional Connectivity Patterns.
Article in IEEE transactions on cybernetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The diagnosis of ASD with MRI: a systematic review and meta-analysis.Translational psychiatry · 2024Pooled it
- rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Dynamically weighted graph neural network for detection of early mild cognitive impairment.PloS one · 2025Article
- Additive interaction between birth asphyxia and febrile seizures on autism spectrum disorder: a population-based study.Molecular autism · 2024Article
- Learning to Fuse Multiple Brain Functional Networks for Automated Autism Identification.Biology · 2023Article
- Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification.Journal of personalized medicine · 2023Article
- A Similarity Measure-Based Approach Using RS-fMRI Data for Autism Spectrum Disorder Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies.IEEE transactions on bio-medical engineering · 2022Article
- Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification.Frontiers in neuroscience · 2022Article
- Brain imaging-based machine learning in autism spectrum disorder: methods and applications.Journal of neuroscience methods · 2021Review
- Artificial intelligence for brain diseases: A systematic review.APL bioengineering · 2020Review
- Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks.Frontiers in neuroscience · 2020Article
- From brain scans to classifiers: A systematic review of ML-based autism diagnostic frameworks.Digital healthReview
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Autism spectrum disorder (ASD) is an age- and sex-related neurodevelopmental disorder that alters the brain's functional connectivity (FC). The changes caused by ASD are associated with different age- and sex-related patterns in neuroimaging data. However, most contemporary computer-assisted ASD diagnosis methods ignore the aforementioned age-/sex-related patterns. In this paper, we propose a novel sparse multiview task-centralized (Sparse-MVTC) ensemble classification method for image-based ASD diagnosis. Specifically, with the age and sex information of each subject, we formulate the classification as a multitask learning problem, where each task corresponds to learning upon a specific age/sex group. We also extract multiview features per subject to better reveal the FC changes. Then, in Sparse-MVTC learning, we select a certain central task and treat the rest as auxiliary tasks. By considering both task-task and view-view relationships between the central task and each auxiliary task, we can learn better upon the entire dataset. Finally, by selecting the central task, in turn, we are able to derive multiple classifiers for each task/group. An ensemble strategy is further adopted, such that the final diagnosis can be integrated for each subject. Our comprehensive experiments on the ABIDE database demonstrate that our proposed Sparse-MVTC ensemble learning can significantly outperform the state-of-the-art classification methods for ASD diagnosis.
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Registered trials
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