2023/08/03 by Inés Gonzalez Pepe, Vinuyan Sivakolunthu, Pepe, Inés Gonzalez +7 · 2 citations
Computer Science · #Advanced Neural Network Applications #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Medical Physics (physics.med-ph) #Neural Networks and Applications #Stochastic Gradient Optimization Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2308.01939
openalex publication_date 2023/08/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper investigates the numerical uncertainty of Convolutional Neural Networks (CNNs) inference for structural brain MRI analysis. It applies Random Rounding -- a stochastic arithmetic technique -- to CNN models employed in non-linear registration (SynthMorph) and whole-brain segmentation (FastSurfer), and compares the resulting numerical uncertainty to the one measured in a reference image-processing pipeline (FreeSurfer recon-all). Results obtained on 32 representative subjects show that CNN predictions are substantially more accurate numerically than traditional image-processing results (non-linear registration: 19 vs 13 significant bits on average; whole-brain segmentation: 0.99 vs 0.92 Sørensen-Dice score on average), which suggests a better reproducibility of CNN results across execution environments.