Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pa...
Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists
About this item
Full title
Author / Creator
Bulten, Wouter , Balkenhol, Maschenka , Belinga, Jean-Joël Awoumou , Brilhante, Américo , Çakır, Aslı , Egevad, Lars , Eklund, Martin , Farré, Xavier , Geronatsiou, Katerina , Molinié, Vincent , Pereira, Guilherme , Roy, Paromita , Saile, Günter , Salles, Paulo , Schaafsma, Ewout , Tschui, Joëlle , Vos, Anne-Marie , ISUP Pathology Imagebase Expert Panel , van Boven, Hester , Vink, Robert , van der Laak, Jeroen , Hulsbergen-van der Kaa, Christina , Litjens, Geert , Delahunt, Brett , Samaratunga, Hemamali , Grignon, David J. , Evans, Andrew J. , M.Berney, Daniel , Pan, Chin-Chen , Kristiansen, Glen , Kench, James G. , Oxley, Jon , Leite, Katia R.M. , McKenney, Jesse K. , Humphrey, Peter A. , Fine, Samson W. , Tsuzuki, Toyonori , Varma, Murali , Zhou, Ming , Comperat, Eva , Bostwick, David G. , Iczkowski, Kenneth A. , Magi-Galluzzi, Cristina , Srigley, John R. , Takahashi, Hiroyuki , van der Kwast, Theo and ISUP Pathology Imagebase Expert Panel
Publisher
New York: Elsevier Inc
Journal title
Language
English
Formats
Publication information
Publisher
New York: Elsevier Inc
Subjects
More information
Scope and Contents
Contents
The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, for...
Alternative Titles
Full title
Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists
Authors, Artists and Contributors
Author / Creator
Balkenhol, Maschenka
Belinga, Jean-Joël Awoumou
Brilhante, Américo
Çakır, Aslı
Egevad, Lars
Eklund, Martin
Farré, Xavier
Geronatsiou, Katerina
Molinié, Vincent
Pereira, Guilherme
Roy, Paromita
Saile, Günter
Salles, Paulo
Schaafsma, Ewout
Tschui, Joëlle
Vos, Anne-Marie
ISUP Pathology Imagebase Expert Panel
van Boven, Hester
Vink, Robert
van der Laak, Jeroen
Hulsbergen-van der Kaa, Christina
Litjens, Geert
Delahunt, Brett
Samaratunga, Hemamali
Grignon, David J.
Evans, Andrew J.
M.Berney, Daniel
Pan, Chin-Chen
Kristiansen, Glen
Kench, James G.
Oxley, Jon
Leite, Katia R.M.
McKenney, Jesse K.
Humphrey, Peter A.
Fine, Samson W.
Tsuzuki, Toyonori
Varma, Murali
Zhou, Ming
Comperat, Eva
Bostwick, David G.
Iczkowski, Kenneth A.
Magi-Galluzzi, Cristina
Srigley, John R.
Takahashi, Hiroyuki
van der Kwast, Theo
ISUP Pathology Imagebase Expert Panel
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_swepub_primary_oai_swepub_ki_se_469155
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_swepub_primary_oai_swepub_ki_se_469155
Other Identifiers
ISSN
0893-3952,1530-0285
E-ISSN
1530-0285
DOI
10.1038/s41379-020-0640-y
