Deep Learning for Classification of Colorectal Polyps on Whole-slide Images
By Bruno Korbar, Andrea M. Olofson, Allen P. Miraflor, Katherine M. Nicka, Matthew A. Suriawinata, Lorenzo Torresani, Arief A. Suriawinata, Saeed Hassanpour. Journal of Pathology Informatics, 2017
307 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/2dc04160a9a77fe7172fa5595b4de2bc2aadc5ca
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Pegah Khosravi
- Ehsan Kazemi
- Olivier Elemento
- Usha R. Acharya
- Christy M Babu
- Swetha Dhamercherla
- M. Ahsan
- Filippo Molinari
In Oncology
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, methodology): “Korbar et al. [131] proposed a colorectal polyp classification on WSI using deep learning.”
- Machine Learning in Drug Discovery: A Review (2021, background): “2016), cancer tumours (Sharma et al. 2017; Korbar et al. 2017; Bychkov et al. 2018; CruzRoa et al. 2017) situated in lung and breast cancers.”
- Artificial intelligence for digital and computational pathology (2023, unknown): “sensitivity from WSIs, allowing the identification of patients who do not require further testing 133 .”
- Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images (2017, background): “…with patients’ clinical data for various cancer types such as prostate cancer (Bhargava et al., 2011; Doyle et al., 2012), lung cancer (Hamilton et al., 2015), breast cancer (Wang et al., 2013; Dong et al., 2014), colorectal cancer (Korbar et al., 2017), and ovarian cancer (Janowczyk et al., 2012).”