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- van der Laak, Jeroen (author)
- Linköpings universitet,Avdelningen för radiologiska vetenskaper,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Klinisk patologi,Radboud Univ Nijmegen, Netherlands
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- Ciompi, Francesco (author)
- Radboud Univ Nijmegen, Netherlands
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- Litjens, Geert (author)
- Radboud Univ Nijmegen, Netherlands
- (creator_code:org_t)
- 2019-10-17
- 2019
- English.
- In: Nature Biomedical Engineering. - : NATURE PUBLISHING GROUP. - 2157-846X. ; 3:11, s. 855-856
- Journal article (other academic/artistic)
Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Publication and Content Type
- vet (subject category)
- art (subject category)
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