ArticlePattern recognition2018
Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker.
Article in Pattern recognition, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review and Meta-analysis of Convolutional Neural Network in the Diagnosis of Colorectal Polyps and Cancer.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2023Pooled it
- Pooled it
- Hybrid deep learning with attention fusion for enhanced colon cancer detection.Scientific reports · 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 frequency attention-embedded network for polyp segmentation.Scientific reports · 2025Article
- Deep learning-based object detection algorithms in medical imaging: Systematic review.Heliyon · 2025Review
- Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset.Health information science and systems · 2024Article
- Recognition of Colon Polyps (Tubular Adenoma, Villous Adenoma) and Normal Colon Epithelium Histomorphology with Transfer Learning.The Eurasian journal of medicine · 2024Article
- A complete benchmark for polyp detection, segmentation and classification in colonoscopy images.Frontiers in oncology · 2024Article
- Artificial intelligence algorithms for real-time detection of colorectal polyps during colonoscopy: a review.American journal of cancer research · 2024Review
- Wireless capsule endoscopy multiclass classification using three-dimensional deep convolutional neural network model.Biomedical engineering online · 2023Article
- Detecting the location of lung cancer on thoracoscopic images using deep convolutional neural networks.Surgery today · 2023Article
- A Temporal Learning Approach to Inpainting Endoscopic Specularities and Its Effect on Image Correspondence.Medical image analysis · 2023Article
- Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques.Sensors (Basel, Switzerland) · 2023Review
- Mobile-PolypNet: Lightweight Colon Polyp Segmentation Network for Low-Resource Settings.Journal of imaging · 2022Article
- Artificial Intelligence in Endoscopy.Digestive diseases and sciences · 2022Review
- On evaluation metrics for medical applications of artificial intelligence.Scientific reports · 2022Article
- SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network.Pattern recognition · 2022Article
- An Adaptive Learning Model for Multiscale Texture Features in Polyp Classification via Computed Tomographic Colonography.Sensors (Basel, Switzerland) · 2022Article
- Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer.World journal of gastrointestinal oncology · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
A computer-aided detection (CAD) tool for locating and detecting polyps can help reduce the chance of missing polyps during colonoscopy. Nevertheless, state-of-the-art algorithms were either computationally complex or suffered from low sensitivity and therefore unsuitable to be used in real clinical setting. In this paper, a novel regression-based Convolutional Neural Network (CNN) pipeline is presented for polyp detection during colonoscopy. The proposed pipeline was constructed in two parts: 1) to learn the spatial features of colorectal polyps, a fast object detection algorithm named ResYOLO was pre-trained with a large non-medical image database and further fine-tuned with colonoscopic images extracted from videos; and 2) temporal information was incorporated via a tracker named Efficient Convolution Operators (ECO) for refining the detection results given by ResYOLO. Evaluated on 17,574 frames extracted from 18 endoscopic videos of the AsuMayoDB, the proposed method was able to detect frames with polyps with a precision of 88.6%, recall of 71.6% and processing speed of 6.5 frames per second, i.e. the method can accurately locate polyps in more frames and at a faster speed compared to existing methods. In conclusion, the proposed method has great potential to be used to assist endoscopists in tracking polyps during colonoscopy.
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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.