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arXiv · 2308.01939

Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis

Inés Gonzalez Pepe·Vinuyan Sivakolunthu·Hae Lang Park·Yohan Chatelain·Tristan Glatard
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Abstract

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 S{\o}rensen-Dice score on average), which suggests a better reproducibility of CNN results across execution environments.

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Inés Gonzalez Pepe, Vinuyan Sivakolunthu, Hae Lang Park, Yohan Chatelain, Tristan Glatard. 2023-08-03. Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis. https://arxiv.org/abs/2308.01939

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