Average test accuracies of models in NC vs. MCI vs. AD classification robustness tests with Gaussian noise (mean = 0) at varying standard deviations (SD) using the ADNI multimodal dataset.

<p>The baseline results correspond to those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0315809#pone.0315809.t007" target="_blank">Table 7</a>, and Gaussian noise was applied to the standardized non-imaging feature vectors of the test dataset, which were standardized using the training dataset. The models were evaluated using stratified nested 5 × 4-fold CV.</p> <p>(PDF)</p>

Publication Details

Journal
Figshare
Published
2024-12-23
DOI
https://doi.org/10.1371/journal.pone.0315809.s003
Primary Topic
Explainable Artificial Intelligence (XAI)
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article
Field-Weighted Citation Impact
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article

Average test accuracies of models in NC vs. MCI vs. AD classification robustness tests with Gaussian noise (mean = 0) at varying standard deviations (SD) using the ADNI multimodal dataset.

Figshare
Explainable Artificial Intelligence (XAI)
article

Average test accuracies of models in NC vs. MCI vs. AD classification robustness tests with Gaussian noise (mean = 0) at varying standard deviations (SD) using the ADNI multimodal dataset.

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Abstract

<p>The baseline results correspond to those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0315809#pone.0315809.t007" target="_blank">Table 7</a>, and Gaussian noise was applied to the standardized non-imaging feature vectors of the test dataset, which were standardized using the training dataset. The models were evaluated using stratified nested 5 × 4-fold CV.</p> <p>(PDF)</p>

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Average test accuracies of models in NC vs. MCI vs. AD classification robustness tests with Gaussian noise (mean = 0) at varying standard deviations (SD) using the ADNI multimodal dataset. · Figshare (2024) | TGRS Research Map | TGRS