deep learning (dl) methods Search Results


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Myomics Inc deep learning (dl) algorithm based on 2d u-net (myomics-t1 software, version 1.0.0)
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Chakshu Research Inc deep-learning methods
The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for <t> AAA classification, </t> when using the best performing combinations of three to six input measurements
Deep Learning Methods, supplied by Chakshu Research Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Mirai INC mammography-based deep learning (dl) model
The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for <t> AAA classification, </t> when using the best performing combinations of three to six input measurements
Mammography Based Deep Learning (Dl) Model, supplied by Mirai INC, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for  AAA classification,  when using the best performing combinations of three to six input measurements

Journal: Biomechanics and Modeling in Mechanobiology

Article Title: Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database

doi: 10.1007/s10237-021-01497-7

Figure Lengend Snippet: The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for AAA classification, when using the best performing combinations of three to six input measurements

Article Snippet: A previous study (Chakshu et al. ) has applied deep-learning methods to AAA classification, using a synthetic data set created by varying seven parameters.

Techniques: Biomarker Discovery