ArticleHuman brain mapping2017
Multi-task diagnosis for autism spectrum disorders using multi-modality features: A multi-center study.
Article in Human brain mapping, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Cross-modal privacy-preserving synthesis and mixture-of-experts ensemble for robust ASD prediction.Frontiers in neuroinformatics · 2025Article
- Bilinear Perceptual Fusion Algorithm Based on Brain Functional and Structural Data for ASD Diagnosis and Regions of Interest Identification.Interdisciplinary sciences, computational life sciences · 2024Article
- Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction.Interdisciplinary sciences, computational life sciences · 2024Article
- Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation.Medical & biological engineering & computing · 2023Article
- Augmented Multicenter Graph Convolutional Network for COVID-19 Diagnosis.IEEE transactions on industrial informatics · 2021Article
- Diagnosis of early Alzheimer's disease based on dynamic high order networks.Brain imaging and behavior · 2021Article
- Discriminative Dictionary Learning for Autism Spectrum Disorder Identification.Frontiers in computational neuroscience · 2021Article
- Tic Detection in Tourette Syndrome Patients Based on Unsupervised Visual Feature Learning.Journal of healthcare engineering · 2021Article
- The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study.JMIR medical informatics · 2020Article
- Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data.Medical image analysis · 2020Article
- Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised Domain-Adaptation Networks Based on Cross-Domain Confounding Representations.Computational and mathematical methods in medicine · 2020Article
- Hierarchical Structured Sparse Learning for Schizophrenia Identification.Neuroinformatics · 2020Article
- Latent Representation Learning for Alzheimer's Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data.IEEE transactions on medical imaging · 2019Article
- Strength and Similarity Guided Group-level Brain Functional Network Construction for MCI Diagnosis.Pattern recognition · 2019Article
- Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model.IEEE transactions on bio-medical engineering · 2019Article
- Characterizing and Predicting Autism Spectrum Disorder by Performing Resting-State Functional Network Community Pattern Analysis.Frontiers in human neuroscience · 2019Article
- Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2018Article
- Multi-Label Nonlinear Matrix Completion With Transductive Multi-Task Feature Selection for Joint MGMT and IDH1 Status Prediction of Patient With High-Grade Gliomas.IEEE transactions on medical imaging · 2018Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.