A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees.
Artificial or augmented intelligence, machine learning, and deep learning will be an increasingly important part of clinical practice for the next generation of radiologists. It is therefore critical that radiology residents develop a practical understanding of deep learning in medical imaging. Cert...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 4; pp. 1026 - 1034 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152559517&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152559517 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2021 vid: 34 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152559517 151641814 152559517 152559517 10.1007/s10278-021-00492-9 152559517 ppf: 1026 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees. aug: au: Wiggins, Walter F. Caton Jr, M. Travis Magudia, Kirti Rosenthal, Michael H. Andriole, Katherine P. affil: Department of Radiology, Brigham & Women's Hospital/Harvard Medical School, Boston, MA, USA sug: subj: Deep Learning Education Radiology Personnel Education Learning Methods Interns and Residents Education Human Internet World Wide Web Congresses and Conferences Education, Medical Diagnostic Imaging Machine Learning Education Radiologists Education ab: Artificial or augmented intelligence, machine learning, and deep learning will be an increasingly important part of clinical practice for the next generation of radiologists. It is therefore critical that radiology residents develop a practical understanding of deep learning in medical imaging. Certain aspects of deep learning are not intuitive and may be better understood through hands-on experience; however, the technical requirements for setting up a programming and computing environment for deep learning can pose a high barrier to entry for individuals with limited experience in computer programming and limited access to GPU-accelerated computing. To address these concerns, we implemented an introductory module for deep learning in medical imaging within a self-contained, web-hosted development environment. Our initial experience established the feasibility of guiding radiology trainees through the module within a 45-min period typical of educational conferences. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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