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COMBINING PHENOTYPIC AND RESTING-STATE FMRI DATA FOR AUTISM CLASSIFICATION WITH RECURRENT NEURAL NETWORKS
Accurate identification of autism spectrum disorder (ASD) from resting-state functional magnetic resonance imaging (rsfMRI) is a challenging task due in large part to the heterogeneity of ASD. Recent work has shown better classification accuracy using a recurrent neural network with rsfMRI time-seri...
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| I publikationen: | Proc IEEE Int Symp Biomed Imaging |
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| Huvudupphovsmän: | , , |
| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
2018
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6166875/ https://ncbi.nlm.nih.gov/pubmed/30288208 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/ISBI.2018.8363676 |
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