http://rdf.ncbi.nlm.nih.gov/pubchem/reference/9490603

Outgoing Links

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contentType Journal Article|Research Support, N.I.H., Extramural
endingPage 92
issn 0895-6111
pageRange 84-92
publicationName Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
startingPage 84
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bibliographicCitation Chen P, Gao L, Shi X, Allen K, Yang L. Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss. Computerized Medical Imaging and Graphics. 2019 Jul;75():84–92. doi: 10.1016/j.compmedimag.2019.06.002.
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date 201907
identifier https://doi.org/10.1016/j.compmedimag.2019.06.002
https://pubmed.ncbi.nlm.nih.gov/PMC9531250
https://pubmed.ncbi.nlm.nih.gov/31238184
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language English
source https://www.crossref.org/
https://pubmed.ncbi.nlm.nih.gov/
title Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss
discussesAsDerivedByTextMining http://rdf.ncbi.nlm.nih.gov/pubchem/disease/DZID8722
http://rdf.ncbi.nlm.nih.gov/pubchem/disease/DZID10569

Total number of triples: 26.