Mitigating analytical variability in fMRI results with style transfer
Elodie Germani Elisa Fromont Camille Maumet
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 space of an auxiliary
classifier that distinguishes statistic maps from different pipelines. We
extend traditional sampling techniques used in DM to improve the transition
performance. Our experiments demonstrate that our proposed methods are
successful: pipelines can indeed be transferred, providing an important source
of data augmentation for future medical studies.