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...

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Bibliographic Details
Published in:Scientific reports 2016-05, Vol.6 (1), p.26286-26286, Article 26286
Main Authors: 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
Format: Article
Language:English
Subjects:
14/63
639/166/985
692/308/575
692/4028/67/1347
692/4028/67/2321
692/4028/67/589/466
Benign
Biopsy
Breast cancer
Breast Neoplasms - diagnosis
Breast Neoplasms - pathology
Diagnosis, Computer-Assisted - methods
Efficiency
Female
Histological Techniques
Humanities and Social Sciences
Humans
Image Interpretation, Computer-Assisted - methods
Lymph nodes
Lymphatic Metastasis - diagnosis
Lymphatic Metastasis - pathology
Machine Learning
Male
Mammography
Medical diagnosis
Metastases
multidisciplinary
Neoplasm Staging - methods
Neural Networks (Computer)
Precision medicine
Prostate cancer
Prostatic Neoplasms - diagnosis
Prostatic Neoplasms - pathology
Science
Sentinel Lymph Node Biopsy
Workloads
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Description
Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/srep26286