# Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics

arXiv: https://arxiv.org/abs/2109.09649 · HTML: https://mlsanity.com/paper/2109.09649/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 52 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [Accounting for Variance in Machine Learning Benchmarks](https://mlsanity.com/paper/2103.03098/) (2021)
- [Cross-validation failure: small sample sizes lead to large error bars](https://mlsanity.com/paper/1706.07581/) (2017)
- [Comparing Perturbation Models for Evaluating Stability of Neuroimaging Pipelines](https://mlsanity.com/paper/1908.10922/) (2019)
- [File-based localization of numerical perturbations in data analysis pipelines](https://mlsanity.com/paper/2006.04684/) (2020)

## Built on by (most cited first, 1 of 1)

- [Uncertain but Useful: Leveraging CNN Training Variability into Data Augmentation](https://mlsanity.com/paper/2509.05238/) (2025)

## Abstract (arXiv)

Machine learning models are commonly applied to human brain imaging datasets in an effort to associate function or structure with behaviour, health, or other individual phenotypes. Such models often rely on low-dimensional maps generated by complex processing pipelines. However, the numerical instabilities inherent to pipelines limit the fidelity of these maps and introduce computational bias. Monte Carlo Arithmetic, a technique for introducing controlled amounts of numerical noise, was used to perturb a structural connectome estimation pipeline, ultimately producing a range of plausible networks for each sample. The variability in the perturbed networks was captured in an augmented dataset, which was then used for an age classification task. We found that resampling brain networks across a series of such numerically perturbed outcomes led to improved performance in all tested classifiers, preprocessing strategies, and dimensionality reduction techniques. Importantly, we find that this benefit does not hinge on a large number of perturbations, suggesting that even minimally perturbing a dataset adds meaningful variance which can be captured in the subsequently designed models.