1 code implementation • 17 Oct 2023 • Neha Gianchandani, Mahsa Dibaji, Johanna Ospel, Fernando Vega, Mariana Bento, M. Ethan MacDonald, Roberto Souza
Brain aging is a regional phenomenon, a facet that remains relatively under-explored within the realm of brain age prediction research using machine learning methods.
no code implementations • 17 Oct 2023 • Mahsa Dibaji, Neha Gianchandani, Akhil Nair, Mansi Singhal, Roberto Souza, Mariana Bento
We found disparities in the performance of brain age prediction models when trained on distinct sex subgroups and datasets, in both final predictions and decision making (assessed using interpretability models).
no code implementations • 18 Sep 2023 • Reza Kakavand, Mehrdad Palizi, Peyman Tahghighi, Reza Ahmadi, Neha Gianchandani, Samer Adeeb, Roberto Souza, W. Brent Edwards, Amin Komeili
The predicted mechanical response of manual and semi-automated FE models were compared.
no code implementations • 23 Aug 2023 • Neha Gianchandani, Mahsa Dibaji, Mariana Bento, Ethan MacDonald, Roberto Souza
Deep learning models have achieved state-of-the-art results in estimating brain age, which is an important brain health biomarker, from magnetic resonance (MR) images.