March 5, 2024, 2:48 p.m. | Fanzhe Yan, Gang Yang, Yu Li, Aiping Liu, Xun Chen

cs.CV updates on arXiv.org arxiv.org

arXiv:2403.01246v1 Announce Type: new
Abstract: Deep learning techniques have demonstrated great potential for accurately estimating brain age by analyzing Magnetic Resonance Imaging (MRI) data from healthy individuals. However, current methods for brain age estimation often directly utilize whole input images, overlooking two important considerations: 1) the heterogeneous nature of brain aging, where different brain regions may degenerate at different rates, and 2) the existence of age-independent redundancies in brain structure. To overcome these limitations, we propose a Dual Graph Attention …

abstract age arxiv attention brain cs.cv current data deep learning deep learning techniques graph images imaging instance mri multiple nature type

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Alternance DATA/AI Engineer (H/F)

@ SQLI | Le Grand-Quevilly, France