Uncertain but Useful: Leveraging CNN Variability into Data Augmentation

• URL: http://arxiv.org/abs/2509.05238v1
• Date: Fri, 05 Sep 2025 16:54:26 GMT
• Title: Uncertain but Useful: Leveraging CNN Variability into Data Augmentation
• Authors: Inés Gonzalez-Pepe, Vinuyan Sivakolunthu, Yohan Chatelain, Tristan Glatard,
• Abstract summary: We investigate the training-time variability using FastSurfer, a CNN-based whole-brain segmentation pipeline.<n>We find that FastSurfer exhibits higher variability compared to that of a traditional neuroimaging pipeline.<n>As a proof of concept, we demonstrate that numerical ensembles can be used as a data augmentation strategy for brain age regression.
• Score: 1.137457877869062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning (DL) is rapidly advancing neuroimaging by achieving state-of-the-art performance with reduced computation times. Yet the numerical stability of DL models -- particularly during training -- remains underexplored. While inference with DL is relatively stable, training introduces additional variability primarily through iterative stochastic optimization. We investigate this training-time variability using FastSurfer, a CNN-based whole-brain segmentation pipeline. Controlled perturbations are introduced via floating point perturbations and random seeds. We find that: (i) FastSurfer exhibits higher variability compared to that of a traditional neuroimaging pipeline, suggesting that DL inherits and is particularly susceptible to sources of instability present in its predecessors; (ii) ensembles generated with perturbations achieve performance similar to an unperturbed baseline; and (iii) variability effectively produces ensembles of numerical model families that can be repurposed for downstream applications. As a proof of concept, we demonstrate that numerical ensembles can be used as a data augmentation strategy for brain age regression. These findings position training-time variability not only as a reproducibility concern but also as a resource that can be harnessed to improve robustness and enable new applications in neuroimaging.

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