ArticleJournal of imaging2023
A Real-Time Polyp-Detection System with Clinical Application in Colonoscopy Using Deep Convolutional Neural Networks.
Article in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Application of colonoscopic auxiliary devices in reducing the polyp miss rate: a prospective randomized controlled study.BMC gastroenterology · 2025Trial
- The Role of Computational Models in the Detection of Colorectal Carcinoma and Precancerous Lesions.International journal of molecular sciences · 2026Review
- Gastrointestinal polyp detection method based on the improved RT-DETR.Scientific reports · 2026Article
- Automated endoscopic image quality assessment for robust colorectal lesion detection.BMC medical imaging · 2025Article
- Revisiting Challenges in Real-world Video Colonoscopy using End-to-End Two Stream Polyp Detection Transformer (TS-PDTR).Journal of medical systems · 2025Article
- A novel approach to overcome black box of AI for optical diagnosis in colonoscopy.Scientific reports · 2025Article
- Edge Artificial Intelligence Device in Real-Time Endoscopy for the Classification of Colonic Neoplasms.Diagnostics (Basel, Switzerland) · 2025Article
- Polyp-Size: A Precise Endoscopic Dataset for AI-Driven Polyp Sizing.Scientific data · 2025Article
- Multiclassification of Colorectal Polyps from Colonoscopy Images Using AI for Early Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- DeepNeXt: a lightweight polyp segmentation algorithm based on multi-scale attention.Quantitative imaging in medicine and surgery · 2024Article
- CRH-YOLO for precise and efficient detection of gastrointestinal polyps.Scientific reports · 2024Article
- MCH-PAN: gastrointestinal polyp detection model integrating multi-scale feature information.Scientific reports · 2024Article
- A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification.Journal of imaging informatics in medicine · 2024Article
- NA-segformer: A multi-level transformer model based on neighborhood attention for colonoscopic polyp segmentation.Scientific reports · 2024Article
- Utilizing adaptive deformable convolution and position embedding for colon polyp segmentation with a visual transformer.Scientific reports · 2024Article
- Artificial intelligence algorithms for real-time detection of colorectal polyps during colonoscopy: a review.American journal of cancer research · 2024Review
- Automated classification of polyps using deep learning architectures and few-shot learning.BMC medical imaging · 2023Article
- A Multiscale Polyp Detection Approach for GI Tract Images Based on Improved DenseNet and Single-Shot Multibox Detector.Diagnostics (Basel, Switzerland) · 2023Article
- Multi-Scale Hybrid Network for Polyp Detection in Wireless Capsule Endoscopy and Colonoscopy Images.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is with a colonoscopy. During this procedure, the gastroenterologist searches for polyps. However, there is a potential risk of polyps being missed by the gastroenterologist. Automated detection of polyps helps to assist the gastroenterologist during a colonoscopy. There are already publications examining the problem of polyp detection in the literature. Nevertheless, most of these systems are only used in the research context and are not implemented for clinical application. Therefore, we introduce the first fully open-source automated polyp-detection system scoring best on current benchmark data and implementing it ready for clinical application. To create the polyp-detection system (ENDOMIND-Advanced), we combined our own collected data from different hospitals and practices in Germany with open-source datasets to create a dataset with over 500,000 annotated images. ENDOMIND-Advanced leverages a post-processing technique based on video detection to work in real-time with a stream of images. It is integrated into a prototype ready for application in clinical interventions. We achieve better performance compared to the best system in the literature and score a F1-score of 90.24% on the open-source CVC-VideoClinicDB benchmark.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.