ArticleJournal of healthcare engineering2017
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images.
Article in Journal of healthcare engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 1 of them a synthesis that pooled it.
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Who cites it
95 citing papers in PubMed, 1 synthesis or guideline pooled it, 798 citations in OpenAlex.
- Demystifying Artificial Intelligence: A Systematic Review of Explainable Artificial Intelligence in Medical Imaging.Sensors (Basel, Switzerland) · 2026Pooled it
- SVF-net: A Dual-encoder Network with Adaptive Gated Fusion for Colorectal Polyp Segmentation.Journal of imaging informatics in medicine · 2026Article
- Synthetic data generation: challenges and perspectives for gastrointestinal medicine.Nature reviews. Gastroenterology & hepatology · 2026Review
- A similarity-aware network with contrastive optimization for biomedical image segmentation.BMC medical imaging · 2026Article
- BAASNet: boundary-aware deep learning for accurate polyp segmentation in colonoscopy.Scientific reports · 2026Article
- GFANet: Global Feature Attention Network for Polyp Segmentation.Journal of imaging informatics in medicine · 2026Article
- GMM-PA: Gaussian Mixture Model-Based Prototype Alignment for Multi-source Domain Adaptation in Polyp Segmentation.Journal of imaging informatics in medicine · 2026Article
- Uncertainty Quantification in Medical Image Segmentation: A Comprehensive Survey.Journal of imaging · 2026Review
- BDKD-Net: Boundary-Probability Knowledge Distillation for Compact Polyp Segmentation.Journal of imaging · 2026Article
- SCA-Net: A Scale- and Contrast-Aware Network for Subtle and Low-Contrast Polyp Segmentation.Journal of imaging informatics in medicine · 2026Article
- PAC-P2T: pyramid atrous convolution with pyramid pooling Transformer for polyp segmentation.Quantitative imaging in medicine and surgery · 2026Article
- MBRSNet: Boundary-Aware Multi-Task Learning with Signed Distance Field Regression for Polyp Segmentation.Journal of imaging · 2026Article
- Leveraging large language and vision models for knowledge extraction from large-scale image-text colonoscopy records.Nature biomedical engineering · 2026Article
- Article
- LightAttn-YOLO-V8: an efficient colorectal polyp detection with lightweight attention mechanism based on YOLO.Scientific reports · 2026Article
- UBENet: Uncertainty Boundary Enhancement Network for Polyp Segmentation in Colonoscopy Images.Journal of imaging informatics in medicine · 2026Article
- Camouflaged object detection via context and texture-aware hierarchical interaction.Scientific reports · 2026Article
- PrysmNet a polyp refining system using salience and multimodal guidance for reproducible cross domain segmentation.NPJ digital medicine · 2026Article
- TransCat: a hybrid CNN-transformer network with KAN for medical image segmentation.Frontiers in bioinformatics · 2026Article
- [Review of application of U-Net and Transformer in colon polyp image segmentation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
35 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.
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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.