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Learning to detect ...
Learning to detect lymphocytes in immunohistochemistry with deep learning
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- Swiderska-Chadaj, Zaneta (författare)
- Radboud Univ Nijmegen, Netherlands
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- Pinckaers, Hans (författare)
- Radboud Univ Nijmegen, Netherlands
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- van Rijthoven, Mart (författare)
- Radboud Univ Nijmegen, Netherlands
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- Balkenhol, Maschenka (författare)
- Radboud Univ Nijmegen, Netherlands
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- Melnikova, Margarita (författare)
- Radboud Univ Nijmegen, Netherlands; Aarhus Univ, Denmark; Randers Reg Hosp, Denmark
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- Geessink, Oscar (författare)
- Radboud Univ Nijmegen, Netherlands
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- Manson, Quirine (författare)
- Univ Med Ctr, Netherlands
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- Sherman, Mark (författare)
- Mayo Clin, FL 32224 USA
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- Polonia, Antonio (författare)
- Univ Porto, Portugal
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- Parry, Jeremy (författare)
- Fiona Stanley Hosp, Australia
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- Abubakar, Mustapha (författare)
- NCI, MD 20892 USA
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- Litjens, Geert (författare)
- Radboud Univ Nijmegen, Netherlands
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- van der Laak, Jeroen (författare)
- 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 (författare)
- Radboud Univ Nijmegen, Netherlands
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(creator_code:org_t)
- ELSEVIER, 2019
- 2019
- Engelska.
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Ingår i: Medical Image Analysis. - : ELSEVIER. - 1361-8415 .- 1361-8423. ; 58
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- The immune system is of critical importance in the development of cancer. The evasion of destruction by the immune system is one of the emerging hallmarks of cancer. We have built a dataset of 171,166 manually annotated CD3(+) and CD8(+) cells, which we used to train deep learning algorithms for automatic detection of lymphocytes in histopathology images to better quantify immune response. Moreover, we investigate the effectiveness of four deep learning based methods when different subcompartments of the whole-slide image are considered: normal tissue areas, areas with immune cell clusters, and areas containing artifacts. We have compared the proposed methods in breast, colon and prostate cancer tissue slides collected from nine different medical centers. Finally, we report the results of an observer study on lymphocyte quantification, which involved four pathologists from different medical centers, and compare their performance with the automatic detection. The results give insights on the applicability of the proposed methods for clinical use. U-Net obtained the highest performance with an F1-score of 0.78 and the highest agreement with manual evaluation (kappa = 0.72), whereas the average pathologists agreement with reference standard was kappa = 0.64. The test set and the automatic evaluation procedure are publicly available at lyon19.grand-challenge.org. (C) 2019 Elsevier B.V. All rights reserved.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- Deep learning; Immune cell detection; Computational pathology; Immunohistochemistry
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Swiderska-Chadaj ...
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Pinckaers, Hans
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van Rijthoven, M ...
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Balkenhol, Masch ...
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Melnikova, Marga ...
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Geessink, Oscar
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visa fler...
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Manson, Quirine
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Sherman, Mark
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Polonia, Antonio
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Parry, Jeremy
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Abubakar, Mustap ...
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Litjens, Geert
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van der Laak, Je ...
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Ciompi, Francesc ...
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- TEKNIK OCH TEKNOLOGIER
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TEKNIK OCH TEKNO ...
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och Medicinteknik
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och Medicinsk bildbe ...
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Medical Image An ...
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Linköpings universitet