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Korbar B, Olofson AM, Miraflor AP, et al. Deep Learning for Classification of Colorectal Polyps on Whole-slide Images. J Pathol Inform. 2017;8:30doi: 10.4103/jpi.jpi_34_17.
Korbar, B., Olofson, A. M., Miraflor, A. P., Nicka, C. M., Suriawinata, M. A., Torresani, L., Suriawinata, A. A., & Hassanpour, S. (2017). Deep Learning for Classification of Colorectal Polyps on Whole-slide Images. Journal of pathology informatics, 830. https://doi.org/10.4103/jpi.jpi_34_17
Korbar, Bruno, et al. "Deep Learning for Classification of Colorectal Polyps on Whole-slide Images." Journal of pathology informatics vol. 8 (2017): 30. doi: https://doi.org/10.4103/jpi.jpi_34_17
Korbar B, Olofson AM, Miraflor AP, Nicka CM, Suriawinata MA, Torresani L, Suriawinata AA, Hassanpour S. Deep Learning for Classification of Colorectal Polyps on Whole-slide Images. J Pathol Inform. 2017 Jul 25;8:30. doi: 10.4103/jpi.jpi_34_17. eCollection 2017. PMID: 28828201; PMCID: PMC5545773.
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J Pathol Inform. 2017 Jul 25;8:30. doi: 10.4103/jpi.jpi_34_17. eCollection 2017.

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

Journal of pathology informatics

Bruno Korbar, Andrea M Olofson, Allen P Miraflor, Catherine M Nicka, Matthew A Suriawinata, Lorenzo Torresani, Arief A Suriawinata, Saeed Hassanpour

Affiliations

  1. Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, One Medical Center Drive, Lebanon, NH 03756, USA.
  2. Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.
  3. Department of Pathology and Laboratory Medicine, Geisel School of Medicine at Dartmouth, One Medical Center Drive, Lebanon, NH 03756, USA.
  4. Department of Epidemiology, Geisel School of Medicine at Dartmouth, One Medical Center Drive, Lebanon, NH 03756, USA.

PMID: 28828201 PMCID: PMC5545773 DOI: 10.4103/jpi.jpi_34_17

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.

Keywords: Colorectal polyps; deep learning; digital pathology; histopathological characterization

Conflict of interest statement

There are no conflicts of interest.

References

  1. Am J Clin Pathol. 2005 Feb;123(2):281-7 - PubMed
  2. J Pathol Inform. 2013 May 30;4:9 - PubMed
  3. Med Image Anal. 2015 Feb;20(1):237-48 - PubMed
  4. Gastrointest Endosc. 2015 Mar;81(3):517-24 - PubMed
  5. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):403-10 - PubMed
  6. Ann Diagn Pathol. 1998 Feb;2(1):79-91 - PubMed
  7. Gastroenterology. 2012 Sep;143(3):844-857 - PubMed
  8. Hum Pathol. 2011 Jan;42(1):1-10 - PubMed
  9. AMIA Annu Symp Proc. 2015 Nov 05;2015 :1899-908 - PubMed
  10. Gastroenterology. 2010 Jun;138(6):2088-100 - PubMed
  11. Phys Med Biol. 2003 Jul 7;48(13):N183-91 - PubMed
  12. IEEE Trans Med Imaging. 2015 Nov;34(11):2366-78 - PubMed
  13. J Pathol Inform. 2016 Jul 26;7:29 - PubMed
  14. Arch Pathol Lab Med. 2006 May;130(5):630-2 - PubMed
  15. IEEE Trans Med Imaging. 2016 May;35(5):1196-1206 - PubMed
  16. Dig Dis Sci. 2015 Mar;60(3):773-80 - PubMed
  17. IEEE Rev Biomed Eng. 2009;2:147-71 - PubMed
  18. Dis Colon Rectum. 2011 Oct;54(10):1216-23 - PubMed
  19. Cancer. 2005 Nov 15;104(10):2205-13 - PubMed
  20. Histopathology. 2009 Jul;55(1):63-6 - PubMed
  21. Nature. 2015 May 28;521(7553):436-44 - PubMed
  22. IEEE Rev Biomed Eng. 2014;7:97-114 - PubMed
  23. Microsc Res Tech. 2002 Oct 15;59(2):109-18 - PubMed
  24. Med Image Anal. 2016 Oct;33:170-175 - PubMed
  25. J Biophotonics. 2013 Jan;6(1):88-100 - PubMed

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