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Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Clinical pathology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Radboud Univ Nijmegen, Netherlands.
Radboud Univ Nijmegen, Netherlands.
Radboud Univ Nijmegen, Netherlands.
2019 (English)In: Nature Biomedical Engineering, E-ISSN 2157-846X, Vol. 3, no 11, p. 855-856Article in journal, Editorial material (Other academic) Published
Abstract [en]

A deep-learning model for cancer detection trained on a large number of scanned pathology slides and associated diagnosis labels enables model development without the need for pixel-level annotations.
Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP , 2019. Vol. 3, no 11, p. 855-856
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Bioinformatics (Computational Biology)
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URN: urn:nbn:se:liu:diva-162548DOI: 10.1038/s41551-019-0472-6ISI: 000496491200007PubMedID: 31624355OAI: oai:DiVA.org:liu-162548DiVA, id: diva2:1376277
Available from: 2019-12-09 Created: 2019-12-09 Last updated: 2021-01-26

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van der Laak, Jeroen
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Division of Radiological SciencesFaculty of Medicine and Health SciencesClinical pathologyCenter for Medical Image Science and Visualization (CMIV)
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