Mitigating analytical variability in fMRI results with style transfer

arXiv:2404.03703v1 Announce Type: cross
Abstract: We propose a novel approach to improve the reproducibility of neuroimaging results by converting statistic maps across different functional MRI pipelines. We make the assumption that pipelines can be considered as a style component of data and propose to use different generative models, among which, Diffusion Models (DM) to convert data between pipelines. We design a new DM-based unsupervised multi-domain image-to-image transition framework and constrain the generation of 3D fMRI statistic maps using the latent …

abstract arxiv cs.ai cs.cv cs.lg data diffusion eess.iv fmri functional generative generative models maps mri neuroimaging novel pipelines reproducibility results style style transfer transfer type