Fader Networks for domain adaptation on fMRI: ABIDE-II study
- URL: http://arxiv.org/abs/2010.07233v1
- Date: Wed, 14 Oct 2020 16:50:50 GMT
- Title: Fader Networks for domain adaptation on fMRI: ABIDE-II study
- Authors: Marina Pominova, Ekaterina Kondrateva, Maxim Sharaev, Alexander
Bernstein, Evgeny Burnaev
- Abstract summary: We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
- Score: 68.5481471934606
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: ABIDE is the largest open-source autism spectrum disorder database with both
fMRI data and full phenotype description. These data were extensively studied
based on functional connectivity analysis as well as with deep learning on raw
data, with top models accuracy close to 75\% for separate scanning sites. Yet
there is still a problem of models transferability between different scanning
sites within ABIDE. In the current paper, we for the first time perform domain
adaptation for brain pathology classification problem on raw neuroimaging data.
We use 3D convolutional autoencoders to build the domain irrelevant latent
space image representation and demonstrate this method to outperform existing
approaches on ABIDE data.
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