Literature DB >> 34327624

A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees.

Walter F Wiggins1,2,3, M Travis Caton4,5, Kirti Magudia4,5, Michael H Rosenthal4, Katherine P Andriole4,5.   

Abstract

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.
© 2021. Society for Imaging Informatics in Medicine.

Entities:  

Keywords:  Deep learning; Machine learning; Medical education; Medical imaging

Mesh:

Year:  2021        PMID: 34327624      PMCID: PMC8455745          DOI: 10.1007/s10278-021-00492-9

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.903


  11 in total

Review 1.  Integrating imaging informatics into the radiology residency curriculum: rationale and example curriculum.

Authors:  Khan M Siddiqui; David L Weiss; Anne P Dunne; Barton F Branstetter
Journal:  J Am Coll Radiol       Date:  2006-01       Impact factor: 5.532

2.  The Need for a Machine Learning Curriculum for Radiologists.

Authors:  Monica J Wood; Neil A Tenenholtz; J Raymond Geis; Mark H Michalski; Katherine P Andriole
Journal:  J Am Coll Radiol       Date:  2018-12-07       Impact factor: 5.532

3.  Preparing Radiologists to Lead in the Era of Artificial Intelligence: Designing and Implementing a Focused Data Science Pathway for Senior Radiology Residents.

Authors:  Walter F Wiggins; M Travis Caton; Kirti Magudia; Sha-Har A Glomski; Elizabeth George; Michael H Rosenthal; Glenn C Gaviola; Katherine P Andriole
Journal:  Radiol Artif Intell       Date:  2020-11-04

4.  Identifying and Addressing Barriers to an Artificial Intelligence Curriculum.

Authors:  Ali S Tejani
Journal:  J Am Coll Radiol       Date:  2020-10-24       Impact factor: 5.532

5.  Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review.

Authors:  Waseem Rawat; Zenghui Wang
Journal:  Neural Comput       Date:  2017-06-09       Impact factor: 2.026

6.  A Survey of Imaging Informatics Fellowships and Their Curricula: Current State Assessment.

Authors:  Brianna L Vey; T S Cook; P Nagy; R J Bruce; R W Filice; K C Wang; N M Safdar
Journal:  J Digit Imaging       Date:  2019-02       Impact factor: 4.056

7.  Toward Augmented Radiologists: Changes in Radiology Education in the Era of Machine Learning and Artificial Intelligence.

Authors:  Shahein H Tajmir; Tarik K Alkasab
Journal:  Acad Radiol       Date:  2018-03-26       Impact factor: 3.173

8.  Artificial Intelligence and Machine Learning in Radiology Education Is Ready for Prime Time.

Authors:  Priscilla J Slanetz; Dania Daye; Po-Hao Chen; Lonie R Salkowski
Journal:  J Am Coll Radiol       Date:  2020-05-16       Impact factor: 5.532

Review 9.  Hello World Deep Learning in Medical Imaging.

Authors:  Paras Lakhani; Daniel L Gray; Carl R Pett; Paul Nagy; George Shih
Journal:  J Digit Imaging       Date:  2018-06       Impact factor: 4.056

10.  AI-RADS: An Artificial Intelligence Curriculum for Residents.

Authors:  Alexander L Lindqwister; Saeed Hassanpour; Petra J Lewis; Jessica M Sin
Journal:  Acad Radiol       Date:  2020-10-15       Impact factor: 3.173

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