ArticleScientific data2023
A multi-centre polyp detection and segmentation dataset for generalisability assessment.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.
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Who cites it
49 citing papers in PubMed, 1 synthesis or guideline pooled it, 135 citations in OpenAlex.
- Deep learning driven colorectal polyp analysis: a review of detection, classification and segmentation methods.Frontiers in artificial intelligence · 2026Pooled it
- Synthetic data generation: challenges and perspectives for gastrointestinal medicine.Nature reviews. Gastroenterology & hepatology · 2026Review
- Impact of expert-curated video training data on computer-aided detection of sessile serrated lesions: a retrospective study in Korea.Clinical endoscopy · 2026Article
- BAASNet: boundary-aware deep learning for accurate polyp segmentation in colonoscopy.Scientific reports · 2026Article
- GEN-Guard: correcting generalization failures for deployable federated surgical AI.International journal of computer assisted radiology and surgery · 2026Article
- Endo-SemiS: Towards Robust Semi-Supervised Image Segmentation for Endoscopic Video.Proceedings of machine learning research · 2026Article
- FD-MSP: feature decoupling network with multi-scale prototypes for domain-adaptive polyp segmentation.BMC medical imaging · 2026Article
- Segment Anything for Video: A Comprehensive Review of Video Object Segmentation and Tracking from Past to Future.Neurocomputing · 2026Article
- CAD-SEL: A dual-modal colonoscopy dataset of subepithelial lesion.Scientific data · 2026Article
- Leveraging large language and vision models for knowledge extraction from large-scale image-text colonoscopy records.Nature biomedical engineering · 2026Article
- Artificial Intelligence in Gastrointestinal Wireless Capsule Endoscopy: A Systematic Literature Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Endo-DET: A Domain-Specific Detection Framework for Multi-Class Endoscopic Disease Detection.Journal of imaging · 2026Article
- CystoDS: a multiclass endoscopy image dataset for artificial intelligence-assisted bladder cancer detection.Scientific data · 2026Article
- PrysmNet a polyp refining system using salience and multimodal guidance for reproducible cross domain segmentation.NPJ digital medicine · 2026Article
- Comparison of colon adenoma detection rate using cap-assisted and artificial intelligence-assisted colonoscopy at a tertiary hospital in the Philippines: a propensity score-matched analysis.Clinical endoscopy · 2026Article
- Efficient and real-time polyp segmentation: An empirical evaluation of lightweight Mamba integration strategies.PloS one · 2026Article
- Deep multimodal state-space fusion of endoscopic-radiomic and clinical data for survival prediction in colorectal cancer.NPJ digital medicine · 2025Article
- Gastrointestinal Lesion Detection Using Ensemble Deep Learning Through Global Contextual Information.Bioengineering (Basel, Switzerland) · 2025Article
- Large language models driven neural architecture search for universal and lightweight disease diagnosis on histopathology slide images.NPJ digital medicine · 2025Article
- Colorectal Polyp Segmentation Based on Deep Learning Methods: A Systematic Review.Journal of imaging · 2025Review
Corrections and comments
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Authors and funding
15 authors at 14 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp's number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as PolypGen) curated by a team of computational scientists and expert gastroenterologists. The paper provides insight into data construction and annotation strategies, quality assurance, and technical validation.
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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.