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Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce ‘deep learning’ as a technique to improve t...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4876324/ https://www.ncbi.nlm.nih.gov/pubmed/27212078 http://dx.doi.org/10.1038/srep26286 |
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author | Litjens, Geert Sánchez, Clara I. Timofeeva, Nadya Hermsen, Meyke Nagtegaal, Iris Kovacs, Iringo Hulsbergen - van de Kaa, Christina Bult, Peter van Ginneken, Bram van der Laak, Jeroen |
author_facet | Litjens, Geert Sánchez, Clara I. Timofeeva, Nadya Hermsen, Meyke Nagtegaal, Iris Kovacs, Iringo Hulsbergen - van de Kaa, Christina Bult, Peter van Ginneken, Bram van der Laak, Jeroen |
author_sort | Litjens, Geert |
collection | PubMed |
description | Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce ‘deep learning’ as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30–40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that ‘deep learning’ holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging. |
format | Online Article Text |
id | pubmed-4876324 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-48763242016-06-06 Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis Litjens, Geert Sánchez, Clara I. Timofeeva, Nadya Hermsen, Meyke Nagtegaal, Iris Kovacs, Iringo Hulsbergen - van de Kaa, Christina Bult, Peter van Ginneken, Bram van der Laak, Jeroen Sci Rep Article Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce ‘deep learning’ as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30–40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that ‘deep learning’ holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging. Nature Publishing Group 2016-05-23 /pmc/articles/PMC4876324/ /pubmed/27212078 http://dx.doi.org/10.1038/srep26286 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Litjens, Geert Sánchez, Clara I. Timofeeva, Nadya Hermsen, Meyke Nagtegaal, Iris Kovacs, Iringo Hulsbergen - van de Kaa, Christina Bult, Peter van Ginneken, Bram van der Laak, Jeroen Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis |
title | Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis |
title_full | Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis |
title_fullStr | Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis |
title_full_unstemmed | Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis |
title_short | Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis |
title_sort | deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4876324/ https://www.ncbi.nlm.nih.gov/pubmed/27212078 http://dx.doi.org/10.1038/srep26286 |
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