ArticleNpj mental health research2024
Identifying autism spectrum disorder from multi-modal data with privacy-preserving.
Article in Npj mental health research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data-Driven Approaches for Autism Detection: A Comprehensive Review of Machine Learning Algorithms and Datasets.International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience · 2026Pooled it
- Construction of a small-sample brain imaging data augmentation and explainable diagnostic model for autism based on generative adversarial networks.BMC medical imaging · 2026Article
- HYPERBOLIC MODEL AGGREGATION FOR FEDERATED LEARNING IN FMRI.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- Integrating Multimodal Neuroimaging and Physical-Health Markers for Autism Spectrum Disorder in the ABCD Study.Journal of integrative neuroscience · 2026Article
- Federated learning for fair autism spectrum disorder screening across age-heterogeneous populations.Frontiers in digital health · 2026Article
- A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data.Bioengineering (Basel, Switzerland) · 2025Review
- Mamba-fusion for privacy-preserving disease prediction.Scientific reports · 2025Article
- Using explainable machine learning and eye-tracking for diagnosing autism spectrum and developmental language disorders in social attention tasks.Frontiers in neuroscience · 2025Article
- Advancing ASD identification with neuroimaging: a novel GARL methodology integrating Deep Q-Learning and generative adversarial networks.BMC medical imaging · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of deep learning models to precision medical diagnosis often requires the aggregation of large amounts of medical data to effectively train high-quality models. However, data privacy protection mechanisms make it difficult to perform medical data collection from different medical institutions. In autism spectrum disorder (ASD) diagnosis, automatic diagnosis using multimodal information from heterogeneous data has not yet achieved satisfactory performance. To address the privacy preservation issue as well as to improve ASD diagnosis, we propose a deep learning framework using multimodal feature fusion and hypergraph neural networks for disease prediction in federated learning (FedHNN). By introducing the federated learning strategy, each local model is trained and computed independently in a distributed manner without data sharing, allowing rapid scaling of medical datasets to achieve robust and scalable deep learning predictive models. To further improve the performance with privacy preservation, we improve the hypergraph model for multimodal fusion to make it suitable for autism spectrum disorder (ASD) diagnosis tasks by capturing the complementarity and correlation between modalities through a hypergraph fusion strategy. The results demonstrate that our proposed federated learning-based prediction model is superior to all local models and outperforms other deep learning models. Overall, our proposed FedHNN has good results in the work of using multi-site data to improve the performance of ASD identification.
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Registered trials
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.