We have 5 of 307 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.
Deep learning models have become essential tools in medical imaging, yet training them end-to-end for diverse tasks remains challenging due to limited labeled data and computational constraints. To address this, we introduce Brain2Vec, a foundation model pre-trained on 74,425 T1-weighted Magnetic Resonance Imaging (MRI...
It is found that WMH are more strongly associated with RcSVD-related structural connectivity changes than with those implicated in dementia, suggesting that WMH may impact structural brain network through different pathways in RcSVD and dementia.
GenFAR, a modular deep learning framework that learns general, clinically informed features from brain MRIs, is introduced, showing that using the learned feature representation can substantially increase the sample efficiency of secondary deep learning training tasks and models, as well as improve their accuracy.
BACKGROUND Demonstrating the specificity of plasma biomarkers to AD-related neurodegeneration would add support to their prognostic and diagnostic clinical use. METHOD Participants from the Baltimore Longitudinal Study of Aging were cognitively unimpaired at the time of their plasma Aβ42/Aβ40, GFAP, NfL (Quanterix Ne...
Among CU individuals, plasma p-tau217 was associated with subsequent MCI/dementia due to AD and showed the most extensive associations with longitudinal AD-related brain atrophy, compared with SPARE-BA or R-indices capturing subcortical, diffuse cortical, or perisylvian atrophy.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
