IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v20y2024i1p1-12n1015.html
   My bibliography  Save this article

Survival analysis using deep learning with medical imaging

Author

Listed:
  • Morrison Samantha

    (Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA)

  • Gatsonis Constantine

    (Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA)

  • Eloyan Ani

    (Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA)

  • Steingrimsson Jon Arni

    (Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA)

Abstract

There is widespread interest in using deep learning to build prediction models for medical imaging data. These deep learning methods capture the local structure of the image and require no manual feature extraction. Despite the importance of modeling survival in the context of medical data analysis, research on deep learning methods for modeling the relationship of imaging and time-to-event data is still under-developed. We provide an overview of deep learning methods for time-to-event outcomes and compare several deep learning methods to Cox model based methods through the analysis of a histology dataset of gliomas.

Suggested Citation

  • Morrison Samantha & Gatsonis Constantine & Eloyan Ani & Steingrimsson Jon Arni, 2024. "Survival analysis using deep learning with medical imaging," The International Journal of Biostatistics, De Gruyter, vol. 20(1), pages 1-12.
    • Handle: RePEc:bpj:ijbist:v:20:y:2024:i:1:p:1-12:n:1015
    DOI: 10.1515/ijb-2022-0113
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ijb-2022-0113
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/ijb-2022-0113?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:bpj:ijbist:v:20:y:2024:i:1:p:1-12:n:1015. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.