ArticleDiagnostics (Basel, Switzerland)2023
Negative Samples for Improving Object Detection-A Case Study in AI-Assisted Colonoscopy for Polyp Detection.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Trial
- PolypSAM-Open: Mitigating Automation Bias in AI-Assisted Colonoscopy via Open-Set Surgical Artifact Rejection.Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.Discover computing · 2026Review
- Clinical Evaluation of PolyDeep, A Computer-Aided Detection System: A Multicenter Randomized Tandem Colonoscopy Trial.Diagnostics (Basel, Switzerland) · 2025Article
- Real-World Colonoscopy Video Integration to Improve Artificial Intelligence Polyp Detection Performance and Reduce Manual Annotation Labor.Diagnostics (Basel, Switzerland) · 2025Article
- A colonic polyps detection algorithm based on an improved YOLOv5s.Scientific reports · 2025Article
- PolyDeep Advance 1: Clinical Validation of a Computer-Aided Detection System for Colorectal Polyp Detection with a Second Observer Design.Diagnostics (Basel, Switzerland) · 2025Article
- REAL-Colon: A dataset for developing real-world AI applications in colonoscopy.Scientific data · 2024Article
- A complete benchmark for polyp detection, segmentation and classification in colonoscopy images.Frontiers in oncology · 2024Article
- Optical diagnosis in still images of colorectal polyps: comparison between expert endoscopists and PolyDeep, a Computer-Aided Diagnosis system.Frontiers in oncology · 2024Article
- Assessing clinical efficacy of polyp detection models using open-access datasets.Frontiers in oncology · 2024Article
- Artificial intelligence in gastrointestinal endoscopy: a comprehensive review.Annals of gastroenterologyReview
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Deep learning object-detection models are being successfully applied to develop computer-aided diagnosis systems for aiding polyp detection during colonoscopies. Here, we evidence the need to include negative samples for both (i) reducing false positives during the polyp-finding phase, by including images with artifacts that may confuse the detection models (e.g., medical instruments, water jets, feces, blood, excessive proximity of the camera to the colon wall, blurred images, etc.) that are usually not included in model development datasets, and (ii) correctly estimating a more realistic performance of the models. By retraining our previously developed YOLOv3-based detection model with a dataset that includes 15% of additional not-polyp images with a variety of artifacts, we were able to generally improve its F1 performance in our internal test datasets (from an average F1 of 0.869 to 0.893), which now include such type of images, as well as in four public datasets that include not-polyp images (from an average F1 of 0.695 to 0.722).
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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.