CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis | Zendy

Marcus A. Badgeley | Zendy; Manway Liu | Zendy; Benjamin S. Glicksberg | Zendy; Mark Shervey | Zendy; John R. Zech | Zendy; Khader Shameer | Zendy; Joseph Lehár | Zendy; Eric K. Oermann | Zendy; Michael V. McConnell | Zendy; Thomas M. Snyder | Zendy; Joel T. Dudley | Zendy

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CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis

Author(s) -

Marcus A. Badgeley,

Manway Liu,

Benjamin S. Glicksberg,

Mark Shervey,

John R. Zech,

Khader Shameer,

Joseph Lehár,

Eric K. Oermann,

Michael V. McConnell,

Thomas M. Snyder,

Joel T. Dudley

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty855

Subject(s) - computer science , segmentation , cad , source code , mit license , computer aided diagnosis , interface (matter) , artificial intelligence , license , code (set theory) , computer aided , software , annotation , machine learning , programming language , engineering drawing , operating system , set (abstract data type) , bubble , maximum bubble pressure method , engineering

Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists' interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems.

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