7 citations · 8 across the 4 of their papers we have counts for
4 papers
Integration of Swin UNETR and statistical shape modeling for a semi-automated segmentation of the knee and biomechanical modeling of articular cartilage
Reza Kakavand, Mehrdad Palizi, Peyman Tahghighi +6
Simulation studies like finite element (FE) modeling provide insight into knee joint mechanics without patient experimentation. Generic FE models represent biomechanical behavior o…
Studying the Effects of Sex-related Differences on Brain Age Prediction using brain MR Imaging
Mahsa Dibaji, Neha Gianchandani, Akhil Nair +3
While utilizing machine learning models, one of the most crucial aspects is how bias and fairness affect model outcomes for diverse demographics. This becomes especially relevant i…
A voxel-level approach to brain age prediction: A method to assess regional brain aging
Neha Gianchandani, Mahsa Dibaji, Johanna Ospel +4
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. Voxel-level p…
Reframing the Brain Age Prediction Problem to a More Interpretable and Quantitative Approach
Neha Gianchandani, Mahsa Dibaji, Mariana Bento +2
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. However, mos…