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Deep Learning for Classification of Colorectal Polyps on Whole-slide Images
Open accessAbstract
CONTEXT: Histopathological characterization of colorectal polyps is critical for determining the risk of colorectal cancer and future rates of surveillance for patients. However, this characterization is a challenging task and suffers from significant inter- and intra-observer variability. AIMS: We built an automatic image analysis method that can accurately classify different types of colorectal polyps on whole-slide images to help pathologists with this characterization and diagnosis. SETTING AND DESIGN: Our method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks. SUBJECTS AND METHODS: Our method covers five common types of polyps (i.e., hyperplastic, sessile serrated, traditional serrated, tubular, and tubulovillous/villous) that are included in the US Multisociety Task Force guidelines for colorectal cancer risk assessment and surveillance. We developed multiple deep-learning approaches by leveraging a dataset of 2074 crop images, which were annotated by multiple domain expert pathologists as reference standards. STATISTICAL ANALYSIS: We evaluated our method on an independent test set of 239 whole-slide images and measured standard machine-learning evaluation metrics of accuracy, precision, recall, and F1 score and their 95% confidence intervals. RESULTS: Our evaluation shows that our method with residual network architecture achieves the best performance for classification of colorectal polyps on whole-slide images (overall accuracy: 93.0%, 95% confidence interval: 89.0%-95.9%). CONCLUSIONS: Our method can reduce the cognitive burden on pathologists and improve their efficacy in histopathological characterization of colorectal polyps and in subsequent risk assessment and follow-up recommendations.
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“Korbar and colleagues (72) developed a deep learning model to classify colorectal polyps into one of five common types based on H&E whole slide images.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [72]
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Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P. E., Verrill, C., Walliander, M., Lundin, M., Haglund, C., & Lundin, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-21758-3 · Cited by 644
Metadata
- Authors
- Korbar, B., Olofson, A. M., Miraflor, A. P., Nicka, C. M., Suriawinata, M. A., Torresani, L., Suriawinata, A. A., & Hassanpour, S.
- Affiliations
- Dartmouth College (US)
- Corresponding author
- Hassanpour, Saeed
- Venue
- Journal of Pathology Informatics
- Publisher
- Elsevier BV
- Published
- 2017-01
- Volume
- 8
- Issue
- 1
- Pages
- 30
- ISSN
- 2153-3539 (ISSN-L)
- PMC
- PMC5545773
- OpenAlex
- W2594760301
- Cited by
- 349
- References
- 53
- Citations by year
- 2026: 16 · 2025: 30 · 2024: 48 · 2023: 41 · 2022: 49 · 2021: 65 · 2020: 51 · 2019: 33 · 2018: 13 · 2017: 3
- FWCI
- 16.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Pathology Informatics (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- Dartmouth College
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- colorectal polyps, whole slide images, histological classification, colorectal cancer risk assessment, hyperplastic polyps, sessile serrated polyps, traditional serrated adenoma, tubular adenoma, villous adenoma, ResNet, computer-aided diagnosis, observer variability
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