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

TL;DR

This paper presents a deep learning-based method for accurately classifying five types of colorectal polyps in whole-slide histology images, aiming to assist pathologists and improve diagnostic efficiency.

Contribution

The study introduces a novel deep learning approach that classifies all five key polyp types per guidelines, achieving high accuracy on independent test samples.

Findings

Achieved 93.0% accuracy in polyp classification

High precision and recall rates (89.7% and 88.3%)

Method reduces pathologists' cognitive load and improves diagnostic efficiency

Abstract

Histopathological characterization of colorectal polyps is an important principle for determining the risk of colorectal cancer and future rates of surveillance for patients. This characterization is time-intensive, requires years of specialized training, and suffers from significant inter-observer and intra-observer variability. In this work, we built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of colorectal polyps. The proposed image-understanding 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. Our image-understanding method covers all five polyp types (hyperplastic polyp, sessile serrated polyp, traditional serrated adenoma, tubular adenoma, and tubulovillous/villous adenoma) that are included in the US multi-society task force guidelines for colorectal cancer risk assessment and surveillance, and encompasses the most common occurrences of colorectal polyps. Our evaluation on 239 independent test samples shows our proposed method can identify the types of colorectal polyps in whole-slide images with a high efficacy (accuracy: 93.0%, precision: 89.7%, recall: 88.3%, F1 score: 88.8%). The presented method in this paper can reduce the cognitive burden on pathologists and improve their accuracy and efficiency in histopathological characterization of colorectal polyps, and in subsequent risk assessment and follow-up recommendations.