Fader networks for domain adaptation on fMRI: ABIDE-II study

Marina Pominova; Ekaterina Kondrateva; Maxim Sharaev; Alexander Bernstein; Evegeny Burnaev

doi:10.1117/12.2587348

4 January 2021 Fader networks for domain adaptation on fMRI: ABIDE-II study

Marina Pominova, Ekaterina Kondrateva, Maxim Sharaev, Alexander Bernstein, Evegeny Burnaev

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116051Z (2021) https://doi.org/10.1117/12.2587348
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

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.

Citation Download Citation

Marina Pominova, Ekaterina Kondrateva, Maxim Sharaev, Alexander Bernstein, and Evegeny Burnaev "Fader networks for domain adaptation on fMRI: ABIDE-II study", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116051Z (4 January 2021); https://doi.org/10.1117/12.2587348

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