Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI

Objective This study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and undersampled MRI in various acquisition protocols. The objective is to determine the validity of differences between different DU...

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Vydáno v:	Magma (New York, N.Y.) Ročník 37; číslo 3; s. 507 - 528
Hlavní autoři:	Sharma, Rishabh, Tsiamyrtzis, Panagiotis, Webb, Andrew G., Leiss, Ernst L., Tsekos, Nikolaos V.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 01.07.2024
Témata:	Algorithms Analysis of Variance Basic Science - Reconstruction algorithms and artificial intelligence Biomedical Engineering and Bioengineering Brain - diagnostic imaging Computer Appl. in Life Sciences Deep Learning Health Informatics Humans Image Enhancement - methods Image Processing, Computer-Assisted - methods Imaging Magnetic Resonance Imaging - methods Medicine Medicine & Public Health Neural Networks, Computer Radiology Reproducibility of Results Research Article Signal-To-Noise Ratio Solid State Physics Deep learning Statistical significance Mixed effects modeling Enhance UNet Undersampled
ISSN:	1352-8661, 1352-8661
On-line přístup:	Získat plný text
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