ArticleDiagnostics (Basel, Switzerland)2021
Polyp Detection from Colorectum Images by Using Attentive YOLOv5.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 87 citations in OpenAlex.
- Evaluation efficacy and accuracy of a real-time computer-aided polyp detection system during colonoscopy: a prospective, multicentric, randomized, parallel-controlled study trial.Surgical endoscopy · 2025Trial
- GAD-YOLO: a gastrointestinal abnormality detection YOLO model with multi-scale channel attention and residual fusion.Medical & biological engineering & computing · 2026Article
- LightAttn-YOLO-V8: an efficient colorectal polyp detection with lightweight attention mechanism based on YOLO.Scientific reports · 2026Article
- Gastrointestinal polyp detection method based on the improved RT-DETR.Scientific reports · 2026Article
- VIM-Polyp: Multimodal Colon Polyp Dataset with Video, Histopathology, and Protein Expression.Scientific data · 2025Article
- Deep Learning for Pathology: YOLOv8 with EigenCAM for Reliable Colorectal Cancer Diagnostics.Bioengineering (Basel, Switzerland) · 2025Article
- Revisiting Challenges in Real-world Video Colonoscopy using End-to-End Two Stream Polyp Detection Transformer (TS-PDTR).Journal of medical systems · 2025Article
- Attention-guided deep framework for polyp localization and subsequent classification via polyp local and Siamese feature fusion.Medical & biological engineering & computing · 2025Article
- Polyp-Size: A Precise Endoscopic Dataset for AI-Driven Polyp Sizing.Scientific data · 2025Article
- Real-World Colonoscopy Video Integration to Improve Artificial Intelligence Polyp Detection Performance and Reduce Manual Annotation Labor.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
- Improving Laryngoscopy Image Analysis Through Integration of Global Information and Local Features in VoFoCD Dataset.Journal of imaging informatics in medicine · 2024Article
- [Colon polyp detection based on multi-scale and multi-level feature fusion and lightweight convolutional neural network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- MCH-PAN: gastrointestinal polyp detection model integrating multi-scale feature information.Scientific reports · 2024Article
- NA-segformer: A multi-level transformer model based on neighborhood attention for colonoscopic polyp segmentation.Scientific reports · 2024Article
- Spatio-Temporal Feature Transformation Based Polyp Recognition for Automatic Detection: Higher Accuracy than Novice Endoscopists in Colorectal Polyp Detection and Diagnosis.Digestive diseases and sciences · 2024Article
- Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence algorithms for real-time detection of colorectal polyps during colonoscopy: a review.American journal of cancer research · 2024Review
- Target-based deep learning network surveillance of non-contrast computed tomography for small infarct core of acute ischemic stroke.Frontiers in neurology · 2024Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundHigh-quality colonoscopy is essential to prevent the occurrence of colorectal cancers. The data of colonoscopy are mainly stored in the form of images. Therefore, artificial intelligence-assisted colonoscopy based on medical images is not only a research hotspot, but also one of the effective auxiliary means to improve the detection rate of adenomas. This research has become the focus of medical institutions and scientific research departments and has important clinical and scientific research value.
methodsIn this paper, we propose a YOLOv5 model based on a self-attention mechanism for polyp target detection. This method uses the idea of regression, using the entire image as the input of the network and directly returning the target frame of this position in multiple positions of the image. In the feature extraction process, an attention mechanism is added to enhance the contribution of information-rich feature channels and weaken the interference of useless channels; Results: The experimental results show that the method can accurately identify polyp images, especially for the small polyps and the polyps with inconspicuous contrasts, and the detection speed is greatly improved compared with the comparison algorithm.
conclusionsThis study will be of great help in reducing the missed diagnosis of clinicians during endoscopy and treatment, and it is also of great significance to the development of clinicians' clinical work.
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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.