Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Open accessAbstract
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.
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Deep CNNs can be successfully used to extract and analyze information obtained from medical images.
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- Authors
- Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen - van de Kaa, C., Bult, P., van Ginneken, B., & van der Laak, J.
- Affiliations
- Radboud University Medical Center (NL)
- Amphia Ziekenhuis (NL)
- Corresponding author
- Litjens, Geert
- Venue
- Scientific Reports
- Published
- 2016-05-23
- Volume
- 6
- Issue
- 1
- Pages
- 26286-26286
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4876324
- OpenAlex
- W2401520370
- Cited by
- 1,069
- References
- 29
- Citations by year
- 2026: 38 · 2025: 81 · 2024: 112 · 2023: 109 · 2022: 139 · 2021: 139 · 2020: 152 · 2019: 142 · 2018: 97 · 2017: 57 · 2016: 2 · 2014: 1
- FWCI
- 81.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- deep learning, histopathological diagnosis, prostate cancer detection, breast cancer metastasis, sentinel lymph node, tumor staging
- MeSH terms
- Machine Learning, Breast Neoplasms, Diagnosis, Computer-Assisted, Female, Histological Techniques, Humans, Image Interpretation, Computer-Assisted, Lymphatic Metastasis, Male, Neoplasm Staging, Prostatic Neoplasms, Neural Networks, Computer, Sentinel Lymph Node Biopsy
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