Deep Metric Representation Learning for Clinical Resting State fMRI.
Annu Int Conf IEEE Eng Med Biol Soc
; 2022: 1-4, 2022 07.
Article
en En
| MEDLINE
| ID: mdl-36086218
With growing size of resting state fMRI datasets and advances in deep learning methods, there are ever increasing opportunities to leverage progress in deep learning to solve challenging tasks in neuroimaging. In this work, we build upon recent advances in deep metric learning, to learn embeddings of rs-fMRI data, which can then be potentially used for several downstream tasks. We propose an efficient training method for our model and compare our method with other widely used models. Our experimental results indicate that deep metric learning can be used as an additional refinement step to learn representations of fMRI data, that significantly improves performance on downstream modeling tasks.
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01-internacional
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MEDLINE
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Imagen por Resonancia Magnética
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En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2022
Tipo del documento:
Article
Pais de publicación:
Estados Unidos