|
Deep learning for classification of colorectal polyps on whole-slide images
Bruno Korbar1, Andrea M Olofson2, Allen P Miraflor2, Catherine M Nicka2, Matthew A Suriawinata2, Lorenzo Torresani3, Arief A Suriawinata2, Saeed Hassanpour4
1 Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, One Medical Center Drive, Lebanon, NH 03756; Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA
2 Department of Pathology and Laboratory Medicine, Geisel School of Medicine at Dartmouth, One Medical Center Drive, Lebanon, NH 03756, USA
3 Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA
4 Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, One Medical Center Drive, Lebanon, NH 03756; Department of Computer Science, Dartmouth College, Hanover, NH 03755; Department of Epidemiology, Geisel School of Medicine at Dartmouth, One Medical Center Drive, Lebanon, NH 03756, USA
Correspondence Address:
Saeed Hassanpour
One Medical Center Drive, HB 7261, Lebanon, NH 03756
USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jpi.jpi_34_17
|
|
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. |
