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

Kiar, Gregory; Chatelain, Yohan; Salari, Ali; Evans, Alan C.; Glatard, Tristan

doi:10.1101/2020.12.16.423084

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…Depending on the analytic goals, this could involve the aggregation of results (e.g., bagging) to generate composite findings, or the ensembling of results to improve prediction 54,55 . This has been recently demonstrated in brain imaging and numerical uncertainty 56,57 .…”

Section: Discussionmentioning

confidence: 79%

Moving Beyond Processing and Analysis-Related Variation in Neuroscience

Giavasis

et al. 2021

Preprint

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When fields lack consensus standards and ground truths for their analytic methods, reproducibility tends to be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools from which scientists can construct processing pipelines and draw interpretations. We provide a critical evaluation of the impact of differences observed in results across five independently developed functional MRI minimal preprocessing pipelines. We show that even when handling the same exact data, inter-pipeline agreement was only moderate, with the specific steps that contribute to the lack of agreement varying across pipeline comparisons. Using a densely sampled test-retest dataset, we show that the limitations imposed by inter-pipeline agreement mainly become appreciable when the reliability of the underlying data is high. We highlight the importance of comparison among analytic tools and parameters, as both widely debated (e.g., global signal regression) and commonly overlooked (e.g., MNI template version) decisions were each found to lead to marked variation. We provide recommendations for incorporating tool-based variability in functional neuroimaging analyses and a supporting infrastructure.

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Section: Discussionmentioning

confidence: 79%

Moving Beyond Processing and Analysis-Related Variation in Neuroscience

Giavasis

et al. 2021

Preprint

Self Cite

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“…The fact that the results observed across OS versions and FL perturbations arise from equally-valid numerical operations also suggests that the observed variability may contain meaningful signal. In particular, signal measured from these perturbations might be leveraged to enhance biomarkers, as suggested in [15] where augmenting a diffusion MRI dataset with numerically-perturbed samples was shown to improve age classification.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic

Salari¹,

Chatelain²,

Kiar³

et al. 2021

Preprint

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Operating system (OS) updates introduce numerical perturbations that impact the reproducibility of computational pipelines. In neuroimaging, this has important practical implications on the validity of computational results, particularly when obtained in systems such as high-performance computing clusters where the experimenter does not control software updates. We present a framework to reproduce the variability induced by OS updates in controlled conditions. We hypothesize that OS updates impact computational pipelines mainly through numerical perturbations originating in mathematical libraries, which we simulate using Monte-Carlo arithmetic in a framework called "fuzzy libmath" (FL). We applied this methodology to pre-processing pipelines of the Human Connectome Project, a flagship open-data project in neuroimaging. We found that FL-perturbed pipelines accurately reproduce the variability induced by OS updates and that this similarity is only mildly dependent on simulation parameters. Importantly, we also found between-subject differences were preserved in both cases, though the between-run variability was of comparable magnitude for both FL and OS perturbations. We found the numerical precision in the HCP preprocessed images to be relatively low, with less than 8 significant bits among the 24 available, which motivates further investigation of the numerical stability of components in the tested pipeline. Overall, our results establish that FL accurately simulates results variability due to OS updates, and is a practical framework to quantify numerical uncertainty in neuroimaging.

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High‐Fidelity Data Augmentation for Few‐Shot Learning in Jet Grout Injection Applications

Atangana Njock,

Yin,

Zhang

2024

Num Anal Meth Geomechanics

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Contemporary geoengineering challenges grapple with the plateauing of both existing algorithms and their depth of insights, a phenomenon exacerbated by the scarcity of high‐fidelity data. Although existing solutions such as Monte‐Carlo method can generate abundant data, they are not sufficiently robust for ensuring the high fidelity of data. This study proposes a novel data augmentation framework that combines statistical and machine learning methods to generate high‐fidelity synthetic data, which closely align with field data in terms of the statistical and empirical attributes. The innovations of the proposed approach lie in the integration of Copulas theory for data generation, a developed geo‐regression anomaly detection (GRAD) for adjusting data attributes, and an evolutionary polynomial regression for data consistency enforcement. The multilayer perceptron (MLP) and a wide‐and‐deep (WaD) networks are applied to assess the effectiveness of high‐fidelity data augmentation using jet grouting data. The outcomes reveal the robustness of the synthetic data generation framework, achieving satisfactory fidelity in both empirical and statistical attributes. The proposed data augmentation improved the R2 and MAE achieved by MLP and WaD up to 28.37% under data fractions ranging from 0.2 to 1. MLP and WaD yielded comparable results in terms of accuracy and generalization ability across various augmented fractions. This indicates that the accuracy of synthetic data plays a pivotal role, suggesting improving data quality can be highly effective in boosting performance, regardless of the model complexity. This study contributes valuable insights to addressing the challenges of scare high‐fidelity data in geoengineering.

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

Cited by 4 publications

References 46 publications

Moving Beyond Processing and Analysis-Related Variation in Neuroscience

Moving Beyond Processing and Analysis-Related Variation in Neuroscience

Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic

High‐Fidelity Data Augmentation for Few‐Shot Learning in Jet Grout Injection Applications

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