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

Marina Pominova1Ekaterina Kondrateva1Maxim Sharaev1Evgeny Burnaev1Alexander Bernstein1

Skolkovo Institute of Science and Technology

Thirteenth International Conference on Machine Vision (MV20)


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.




Copy bibtex


If you have any questions about this work, please contact us under ekaterina.kondrateva@skoltech.ru.