Výsledky vyhľadávania - "Variational autoencoder network"

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  1. 1

    Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability Autor Lu, Jiayu, Yan, Tianyi, Yang, Lan, Zhang, Xi, Li, Jiaxin, Li, Dandan, Xiang, Jie, Wang, Bin

    ISSN: 1053-8119, 1095-9572, 1095-9572
    Vydavateľské údaje: United States Elsevier Inc 15.07.2024
    Vydané v NeuroImage (Orlando, Fla.) (15.07.2024)
    “…•High inter-subject variability for brain fingerprinting and cognitive behavior predicting…”
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    Journal Article
  2. 2

    Learning a Probabilistic Model for Diffeomorphic Registration Autor Krebs, Julian, Delingette, Herve, Mailhe, Boris, Ayache, Nicholas, Mansi, Tommaso

    ISSN: 0278-0062, 1558-254X, 1558-254X
    Vydavateľské údaje: United States IEEE 01.09.2019
    Vydané v IEEE transactions on medical imaging (01.09.2019)
    “… image. Our unsupervised method is based on the variational inference. In particular, we use a conditional variational autoencoder network and constrain transformations to be symmetric…”
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    Journal Article
  3. 3

    Spatio-Temporal Classification of Lung Ventilation Patterns using 3D EIT Images: A General Approach for Individualized Lung Function Evaluation Autor Chen, Shuzhe, Li, Li, Lin, Zhichao, Zhang, Ke, Gong, Ying, Wang, Lu, Wu, Xu, Li, Maokun, Song, Yuanlin, Yang, Fan, Xu, Shenheng

    ISSN: 2331-8422
    Vydavateľské údaje: Ithaca Cornell University Library, arXiv.org 01.07.2023
    Vydané v arXiv.org (01.07.2023)
    “… The study uses a Variational Autoencoder network with a MultiRes block to compress the spatial distribution in a 3D image into a one…”
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  4. 4

    Functional Connectivity Networks with Latent Distributions for Mild Cognitive Impairment Identification Autor Tang, Qiling, Lu, Yuhong, Cai, Bilian, Wang, Yan

    ISSN: 0897-1889, 1618-727X, 1618-727X
    Vydavateľské údaje: United States Springer Nature B.V 01.10.2023
    Vydané v Journal of digital imaging (01.10.2023)
    “… Due to the complexity and variability of rs-fMRI signal, we consider it as a random variable, and utilize variational autoencoder networks to encode it as a confidence distribution in the latent…”
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    Journal Article