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encoder-decoder neural network with 3d squeeze-and-excitation and deep supervision  (IEEE Access)

 
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    IEEE Access encoder-decoder neural network with 3d squeeze-and-excitation and deep supervision
    Encoder Decoder Neural Network With 3d Squeeze And Excitation And Deep Supervision, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/3d+encoder/encoder+decoder+neural+network+with+3d+squeeze+and+excitation+and+deep+supervision/pm37897988-459-6-15
    Average 90 stars, based on 1 article reviews
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    other:

    Article Title: Multi-scale context UNet-like network with redesigned skip connections for medical image segmentation.
    Article Snippet: a College of Mathematics and Physics, Wenzhou University, Wenzhou, 325035, Zhejiang, China b The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China c College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, Zhejiang, China d Key Laboratory of Intelligent Image Processing and Analysis, Wenzhou, 325035, Zhejiang, China e Information Technology Center, Wenzhou University, Wenzhou, 325035, Zhejiang, China f College of AI Convergence, Chonnam National University, Gwangju, 61186, Korea



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    Image Search Results


    Ablation study: performance measures ± standard error of the mean. ‘ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\checkmark$\end{document} ’ indicates included, ‘×’ indicates excluded model components; the first two rows are the complete CANDELA model with the two different pre-training stragies. Models marked ‘ † ’ are significantly worse than CANDELA using only one pre-training task. Models marked ‘*’ are significantly better (pairwise t -test, α = 0.05, corrected for multiple testing using Holm–Šídák). Bold values indicate the best configuration

    Journal: NAR Genomics and Bioinformatics

    Article Title: Cancer drug sensitivity estimation using modular deep Graph Neural Networks

    doi: 10.1093/nargab/lqae043

    Figure Lengend Snippet: Ablation study: performance measures ± standard error of the mean. ‘ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\checkmark$\end{document} ’ indicates included, ‘×’ indicates excluded model components; the first two rows are the complete CANDELA model with the two different pre-training stragies. Models marked ‘ † ’ are significantly worse than CANDELA using only one pre-training task. Models marked ‘*’ are significantly better (pairwise t -test, α = 0.05, corrected for multiple testing using Holm–Šídák). Bold values indicate the best configuration

    Article Snippet: As an alternative to pre-training the drug encoder on matabolite properties and toxicity, we have studied the use of the 3D Infomax encoding for drugs.

    Techniques: