Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods

Handelman, Guy S., Kok, Hong Kuan, Chandra, Ronil V., Razavi, Amir H., Huang, Shiwei, Brooks, Mark, Lee, Michael J. and Asadi, Hamed 2019, Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods, AJR. American journal of roentgenology, vol. 212, no. 1, pp. 38-43, doi: 10.2214/AJR.18.20224.


Title Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods
Author(s) Handelman, Guy S.
Kok, Hong Kuan
Chandra, Ronil V.
Razavi, Amir H.
Huang, Shiwei
Brooks, Mark
Lee, Michael J.
Asadi, HamedORCID iD for Asadi, Hamed orcid.org/0000-0003-2475-9727
Journal name AJR. American journal of roentgenology
Volume number 212
Issue number 1
Start page 38
End page 43
Total pages 6
Publisher American Roentgen Ray Society
Place of publication Leesburg, Va.
Publication date 2019-01
ISSN 0361-803X
1546-3141
Keyword(s) artificial intelligence
machine learning
medicine
supervised machine learning
unsupervised machine learning
Science & Technology
Life Sciences & Biomedicine
Radiology, Nuclear Medicine & Medical Imaging
OPERATING CHARACTERISTIC CURVES
DIAGNOSIS
VALIDATION
Language eng
DOI 10.2214/AJR.18.20224
Field of Research 1103 Clinical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, American Roentgen Ray Society
Persistent URL http://hdl.handle.net/10536/DRO/DU:30115644

Document type: Journal Article
Collection: School of Medicine
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