Cited by 349AI in cancer detectionColorectal Cancer Screening and DetectionAdvanced Neural Network Applications

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Deep Learning for Classification of Colorectal Polyps on Whole-slide Images

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Korbar, Bruno, Olofson, Andrea M., Miraflor, Allen P, Nicka, Catherine M., Suriawinata, Matthew A., Torresani, Lorenzo, Suriawinata, Arief A., Hassanpour, Saeed·2017·Journal of Pathology Informatics

DOI 10.4103/jpi.jpi_34_17PMC PMC5545773

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Abstract

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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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
ORCID: Miraflor, Allen P
Torresani, Lorenzo
Suriawinata, Arief A.
Hassanpour, Saeed
Venue: Journal of Pathology Informatics
Publisher: Elsevier BV
Published: 2017-01
Volume: 8
Issue: 1
Pages: 30
ISSN: 2153-3539 (ISSN-L)
DOI: 10.4103/jpi.jpi_34_17
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: cc-by-nc-sa
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-sa/3.0/ (vor, 1930 d)
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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Deep Learning for Classification of Colorectal Polyps on Whole-slide Images

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