3 citations · 6 across the 5 of their papers we have counts for
5 papers
Conservative & Aggressive NaNs Accelerate U-Nets for Neuroimaging
Inés Gonzalez-Pepe, Vinuyan Sivakolunthu, Jacob Fortin +2
Deep learning models for neuroimaging increasingly rely on large architectures, making efficiency a persistent concern despite advances in hardware. Through an analysis of numerica…
Uncertain but Useful: Leveraging CNN Training Variability into Data Augmentation
Inés Gonzalez-Pepe, Vinuyan Sivakolunthu, Yohan Chatelain +1
Deep learning (DL) has transformed neuroimaging by delivering state-of-the-art performance with reduced computation times. Yet, the numerical uncertainty inherent to DL training re…
Numerical Uncertainty in Linear Registration: An Experimental Study
Niusha Mirhakimi, Yohan Chatelain, Tristan Glatard +1
While linear registration is a critical step in MRI preprocessing pipelines, its numerical uncertainty is understudied. Using Monte-Carlo Arithmetic (MCA) simulations, we assessed…
An Analysis of Performance Bottlenecks in MRI Pre-Processing
Mathieu Dugré, Yohan Chatelain, Tristan Glatard
Magnetic Resonance Image (MRI) pre-processing is a critical step for neuroimaging analysis. However, the computational cost of MRI pre-processing pipelines is a major bottleneck fo…
Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study
Elodie Germani, Nikhil Baghwat, Mathieu Dugré +7
Parkinson's disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for pre…