We have 3 of 12 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
A Geometric-to-Semantic Spherical Transfer Learning framework that outperforms fully supervised baselines trained from scratch on variable and tertiary sulci, confirming that learning the cortex shape is highly beneficial for identifying its rarest parts.
Champollion, a self-supervised learning framework that learns interpretable local representations of cortical folding from structural MRI, is introduced, establishing cortical folding as a rich and largely untapped source of neurodevelopmental information.
Self-supervised representations of cortical folding are established as a powerful phenotype for the genetic study of neurodevelopment through multivariate genome-wide association studies (GWAS).
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
