ArticleIEEE access : practical innovations, open solutions2021
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07391514 (Deep Learning-Based Intraoperative Dual-tracer Video Analysis of Sentinel Lymph Node Mapping for Metastasis Prediction in cN0 Papillary Thyroid Carcinoma), which is not on this map. Cited by 56 papers, 1 of them a synthesis 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.
Deep Learning-Based Intraoperative Dual-tracer Video Analysis of Sentinel Lymph Node Mapping for Metastasis Prediction in cN0 Papillary Thyroid Carcinoma: A Prospective Cohort Study
Who cites it
56 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of machine learning models for management, prediction and classification of ARDS.Respiratory research · 2024Pooled it
- Deep Learning for Automatic Segmentation of Pituitary Adenomas: A Videomics Study.Operative neurosurgery (Hagerstown, Md.) · 2026Article
- Dual-Tracer Imaging and Deep Learning for Real-Time Prediction of Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma.Cancers · 2026Article
- Evaluation of Net Withdrawal Time and Colonoscopy Video Summarization Using Deep Learning Based Automated Temporal Video Segmentation.Journal of imaging informatics in medicine · 2026Article
- TransCat: a hybrid CNN-transformer network with KAN for medical image segmentation.Frontiers in bioinformatics · 2026Article
- Artificial intelligence for perioperative precision in surgical oncology.Frontiers in surgery · 2026Review
- MAPSeg: self-supervised colorectal polyp segmentation via memory-augmented framework and synthetic polyp simulation.Frontiers in digital health · 2026Article
- BAR-UNet: a boundary-aware and appearance-robust framework for polyp segmentation.Frontiers in oncology · 2026Article
- Comparison of deep learning models for real-time neural tissue segmentation in spinal endoscopy.BMC medical imaging · 2025Article
- Attention-guided deep framework for polyp localization and subsequent classification via polyp local and Siamese feature fusion.Medical & biological engineering & computing · 2025Article
- Colorectal Polyp Segmentation Based on Deep Learning Methods: A Systematic Review.Journal of imaging · 2025Review
- ColonNeXt: Fully Convolutional Attention for Polyp Segmentation.Journal of imaging informatics in medicine · 2025Article
- Colorectal cancer unmasked: A synergistic AI framework for Hyper-granular image dissection, precision segmentation, and automated diagnosis.BMC medical imaging · 2025Article
- Real-World Colonoscopy Video Integration to Improve Artificial Intelligence Polyp Detection Performance and Reduce Manual Annotation Labor.Diagnostics (Basel, Switzerland) · 2025Article
- Attention-Enhanced Multi-Task Deep Learning Model for Classification and Segmentation of Esophageal Lesions.ACS omega · 2025Article
- Deep Learning-Based Semantic Segmentation for Objective Colonoscopy Quality Assessment.Journal of imaging · 2025Article
- A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx.Scientific reports · 2025Article
- Introducing a Deep Neural Network Model with Practical Implementation for Polyp Detection in Colonoscopy Videos.Journal of medical signals and sensors · 2025Article
- PlutoNet: An efficient polyp segmentation network with modified partial decoder and decoder consistency training.Healthcare technology letters · 2024Article
- UViT-Seg: An Efficient ViT and U-Net-Based Framework for Accurate Colorectal Polyp Segmentation in Colonoscopy and WCE Images.Journal of imaging informatics in medicine · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible and provide a fair comparison of developed methods. In this paper, we benchmark several recent state-of-the-art methods using Kvasir-SEG, an open-access dataset of colonoscopy images for polyp detection, localisation, and segmentation evaluating both method accuracy and speed. Whilst, most methods in literature have competitive performance over accuracy, we show that the proposed ColonSegNet achieved a better trade-off between an average precision of 0.8000 and mean IoU of 0.8100, and the fastest speed of 180 frames per second for the detection and localisation task. Likewise, the proposed ColonSegNet achieved a competitive dice coefficient of 0.8206 and the best average speed of 182.38 frames per second for the segmentation task. Our comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimise miss-detection rates.
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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.