IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/p9mzc.html
   My bibliography  Save this paper

Attention Gated Deep Convolutional Autoencoder for Brain Tumor Segmentation

Author

Listed:
  • Amin, Ramzan

Abstract

Attention Gated Deep Convolutional Autoencoder for Brain Tumor Segmentation

Suggested Citation

  • Amin, Ramzan, 2022. "Attention Gated Deep Convolutional Autoencoder for Brain Tumor Segmentation," OSF Preprints p9mzc, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:p9mzc
    DOI: 10.31219/osf.io/p9mzc
    as

    Download full text from publisher

    File URL: https://osf.io/download/637c775fc4123301a267ec64/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/p9mzc?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Constantin BRATIANU & Dan PAIUC, 2022. "A Bibliometric Analysis of Cultural Intelligence and Multicultural Leadership," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 23(3), pages 319-337, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Constantin Bratianu & Dan Paiuc, 2023. "Diversity And Inclusion Within Multicultural Leadership In The Covid Years: A Bibliometric Study 2019-2022," Oradea Journal of Business and Economics, University of Oradea, Faculty of Economics, vol. 8(1), pages 40-51, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:p9mzc. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.