# Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations

arXiv: https://arxiv.org/abs/2104.10824 · HTML: https://mlsanity.com/paper/2104.10824/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 83 references that are in the map.
- First posted on arXiv 22 Apr 2021.

## Builds on (most cited first, 30 of 41)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [Domain Adaptive Faster R-CNN for Object Detection in the Wild](https://mlsanity.com/paper/1803.03243/) (2018)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Dilated Residual Networks](https://mlsanity.com/paper/1705.09914/) (2017)
- [Mask Scoring R-CNN](https://mlsanity.com/paper/1903.00241/) (2019)
- [Single-Shot Refinement Neural Network for Object Detection](https://mlsanity.com/paper/1711.06897/) (2017)
- [M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network](https://mlsanity.com/paper/1811.04533/) (2018)
- [Augmenting Colonoscopy using Extended and Directional CycleGAN for Lossy Image Translation](https://mlsanity.com/paper/2003.12473/) (2020)
- [DetNet: A Backbone network for Object Detection](https://mlsanity.com/paper/1804.06215/) (2018)
- [Deep Feature Augmentation for Occluded Image Classification](https://mlsanity.com/paper/2011.00768/) (2020)
- [MDFN: Multi-Scale Deep Feature Learning Network for Object Detection](https://mlsanity.com/paper/1912.04514/) (2019)

## Built on by (most cited first, 30 of 38)

- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-train](https://mlsanity.com/paper/2306.16741/) (2023)
- [Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation](https://mlsanity.com/paper/2105.00999/) (2021)
- [Self-Supervised Learning for Endoscopic Video Analysis](https://mlsanity.com/paper/2308.12394/) (2023)
- [GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection](https://mlsanity.com/paper/2307.08140/) (2023)
- [REAL-Colon: A dataset for developing real-world AI applications in colonoscopy](https://mlsanity.com/paper/2403.02163/) (2024)
- [LEMON: A Large Endoscopic MONocular Dataset and Foundation Model for Perception in Surgical Settings](https://mlsanity.com/paper/2503.19740/) (2025)
- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [An Unsupervised Domain Adaptation Model based on Dual-module Adversarial Training](https://mlsanity.com/paper/2112.15555/) (2021)
- [Aggregating Global Features into Local Vision Transformer](https://mlsanity.com/paper/2201.12903/) (2022)
- [BlazeNeo: Blazing fast polyp segmentation and neoplasm detection](https://mlsanity.com/paper/2203.00129/) (2022)
- [A Domain Gap Aware Generative Adversarial Network for Multi-domain Image Translation](https://mlsanity.com/paper/2110.10837/) (2021)
- [Agentic AI framework for End-to-End Medical Data Inference](https://mlsanity.com/paper/2507.18115/) (2025)
- [GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification](https://mlsanity.com/paper/2205.02840/) (2022)
- [Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs](https://mlsanity.com/paper/2201.09396/) (2022)
- [On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild](https://mlsanity.com/paper/2307.10267/) (2023)
- [Aphid Cluster Recognition and Detection in the Wild Using Deep Learning Models](https://mlsanity.com/paper/2308.05881/) (2023)
- [A New Dataset and Comparative Study for Aphid Cluster Detection](https://mlsanity.com/paper/2307.05929/) (2023)
- [A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields](https://mlsanity.com/paper/2405.04305/) (2024)
- [PolypSegTrack: Unified Foundation Model for Colonoscopy Video Analysis](https://mlsanity.com/paper/2503.24108/) (2025)
- [One VLM, Two Roles: Stage-Wise Routing and Specialty-Level Deployment for Clinical Workflows](https://mlsanity.com/paper/2508.16839/) (2025)
- [Gender, Smoking History and Age Prediction from Laryngeal Images](https://mlsanity.com/paper/2305.16661/) (2023)
- [IBoxCLA: Towards Robust Box-supervised Segmentation of Polyp via Improved Box-dice and Contrastive Latent-anchors](https://mlsanity.com/paper/2310.07248/) (2023)
- [Kitchen Food Waste Image Segmentation and Classification for Compost Nutrients Estimation](https://mlsanity.com/paper/2401.15175/) (2024)
- [Multi-Layer Dense Attention Decoder for Polyp Segmentation](https://mlsanity.com/paper/2403.18180/) (2024)
- [VAP-Diffusion: Enriching Descriptions with MLLMs for Enhanced Medical Image Generation](https://mlsanity.com/paper/2506.23641/) (2025)
- [Predicting Mitral Valve mTEER Surgery Outcomes Using Machine Learning and Deep Learning Techniques](https://mlsanity.com/paper/2401.13197/) (2024)
- [Deep Learning for Improved Polyp Detection from Synthetic Narrow-Band Imaging](https://mlsanity.com/paper/2401.13315/) (2024)
- [Medical Imaging Complexity and its Effects on GAN Performance](https://mlsanity.com/paper/2410.17959/) (2024)

## Abstract (arXiv)

Colorectal cancer (CRC) is one of the most common types of cancer with a high mortality rate. Colonoscopy is the preferred procedure for CRC screening and has proven to be effective in reducing CRC mortality. Thus, a reliable computer-aided polyp detection and classification system can significantly increase the effectiveness of colonoscopy. In this paper, we create an endoscopic dataset collected from various sources and annotate the ground truth of polyp location and classification results with the help of experienced gastroenterologists. The dataset can serve as a benchmark platform to train and evaluate the machine learning models for polyp classification. We have also compared the performance of eight state-of-the-art deep learning-based object detection models. The results demonstrate that deep CNN models are promising in CRC screening. This work can serve as a baseline for future research in polyp detection and classification.