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On the assessment of deep-learning based super-resolution in small datasets of human brain MRI scans
2026-02-17
radiology and imaging
Title + abstract only
View on medRxiv
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Deep-learning based super-resolution has shown promise for enhancing the spatial resolution of brain magnetic resonance images, which may help visualize small anatomical structures more clearly. However, when only limited training data are available, it remains uncertain which model assessment method provides the most reliable estimate of out-of-sample performance. In this study, three widely used assessment strategies (three-way holdout, k-fold cross-validation, and nested cross-validation) wer...
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