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

arXiv: https://arxiv.org/abs/1703.01550 · HTML: https://mlsanity.com/paper/1703.01550/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2017.

## Built on by (most cited first, 26 of 26)

- [Artificial Intelligence for Digital and Computational Pathology](https://mlsanity.com/paper/2401.06148/) (2023)
- [A Petri Dish for Histopathology Image Analysis](https://mlsanity.com/paper/2101.12355/) (2021)
- [A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches](https://mlsanity.com/paper/2102.10553/) (2021)
- [UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading](https://mlsanity.com/paper/2101.09991/) (2021)
- [Inference of captions from histopathological patches](https://mlsanity.com/paper/2202.03432/) (2022)
- [Resolution-Based Distillation for Efficient Histology Image Classification](https://mlsanity.com/paper/2101.04170/) (2021)
- [Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Review](https://mlsanity.com/paper/2502.14886/) (2025)
- [A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers](https://mlsanity.com/paper/2104.06243/) (2021)
- [Evaluating histopathology transfer learning with ChampKit](https://mlsanity.com/paper/2206.06862/) (2022)
- [Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review](https://mlsanity.com/paper/2301.12222/) (2023)
- [On the Efficiency of Subclass Knowledge Distillation in Classification Tasks](https://mlsanity.com/paper/2109.05587/) (2021)
- [Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning](https://mlsanity.com/paper/2101.03285/) (2021)
- [Histogram of Cell Types: Deep Learning for Automated Bone Marrow Cytology](https://mlsanity.com/paper/2107.02293/) (2021)
- [Calibrating Histopathology Image Classifiers using Label Smoothing](https://mlsanity.com/paper/2201.11866/) (2022)
- [Subclass Knowledge Distillation with Known Subclass Labels](https://mlsanity.com/paper/2207.08063/) (2022)
- [Deep Learning-Based Open Source Toolkit for Eosinophil Detection in Pediatric Eosinophilic Esophagitis](https://mlsanity.com/paper/2308.06333/) (2023)
- [HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning](https://mlsanity.com/paper/2209.06185/) (2022)
- [UltraLight Med-Vision Mamba for Classification of Neoplastic Progression in Tubular Adenomas](https://mlsanity.com/paper/2508.09339/) (2025)
- [Dysplasia grading of colorectal polyps through CNN analysis of WSI](https://mlsanity.com/paper/2102.05498/) (2021)
- [A vision transformer-based framework for knowledge transfer from multi-modal to mono-modal lymphoma subtyping models](https://mlsanity.com/paper/2308.01328/) (2023)
- [A deep learning model for gastric diffuse-type adenocarcinoma classification in whole slide images](https://mlsanity.com/paper/2104.12478/) (2021)
- [Automated risk classification of colon biopsies based on semantic segmentation of histopathology images](https://mlsanity.com/paper/2109.07892/) (2021)
- [Automated identification and quantification of myocardial inflammatory infiltration in digital histological images to diagnose myocarditis](https://mlsanity.com/paper/2307.01098/) (2023)
- [Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records](https://mlsanity.com/paper/2410.09880/) (2024)
- [Decoding Future Risk: Deep Learning Analysis of Tubular Adenoma Whole-Slide Images](https://mlsanity.com/paper/2602.09155/) (2026)
- [Performance and Interpretability of Convolutional, Transformer, and Hybrid Deep Learning Models in Colorectal Histology Classification](https://mlsanity.com/paper/2606.23744/) (2026)

## Abstract (arXiv)

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.