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Endoscopic Vision Challenge

https://doi.org/10.5281/zenodo.19697092
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-10470-6_23openalex-extracted
Automatic Polyp Detection Using Global Geometric Constraints and Local Intensity Variation Patterns
inferredcites →https://doi.org/10.1007/978-3-319-24553-9_49openalex-extracted
Visibility Map: A New Method in Evaluation Quality of Optical Colonoscopy
inferredcites →https://doi.org/10.1007/978-3-642-25719-3_2openalex-extracted
Improved Curvature Estimation for Shape Analysis in Computer-Aided Detection of Colonic Polyps
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-extracted
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1007/s11548-013-0926-3openalex-extracted
Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer
inferredcites →https://doi.org/10.1016/j.compmedimag.2015.02.007openalex-extracted
WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians
inferredcites →https://doi.org/10.1016/j.gie.2007.10.018openalex-extracted
An adequate level of training for technical competence in screening and diagnostic colonoscopy: a prospective multicenter evaluation of the learning curve
inferredcites →https://doi.org/10.1016/j.gie.2011.01.051openalex-extracted
The impact of suboptimal bowel preparation on adenoma miss rates and the factors associated with early repeat colonoscopy
inferredcites →https://doi.org/10.1016/j.gie.2011.08.002openalex-extracted
Accuracy of in vivo optical diagnosis of colon polyp histology by narrow-band imaging in predicting colonoscopy surveillance intervals
inferredcites →https://doi.org/10.1016/j.procs.2016.07.017openalex-extracted
Active Learning for Real Time Detection of Polyps in Videocolonoscopy
inferredcites →https://doi.org/10.1038/nrgastro.2015.13openalex-extracted
Software for enhanced video capsule endoscopy: challenges for essential progress
inferredcites →https://doi.org/10.1055/s-0033-1344548openalex-extracted
Post-polypectomy colonoscopy surveillance: European Society of Gastrointestinal Endoscopy (ESGE) Guideline
inferredcites →https://doi.org/10.1055/s-2004-826040openalex-extracted
Narrow-Band Imaging in the Diagnosis of Colorectal Mucosal Lesions: A Pilot Study
inferredcites →https://doi.org/10.1056/nejmoa0800996openalex-extracted
Accuracy of CT Colonography for Detection of Large Adenomas and Cancers
inferredcites →https://doi.org/10.1109/ccece.2003.1226181openalex-extracted
Real-time image processing system for endoscopic applications
inferredcites →https://doi.org/10.1109/icip.2007.4379193openalex-extracted
Polyp Detection in Colonoscopy Video using Elliptical Shape Feature
inferredcites →https://doi.org/10.1109/isbi.2015.7163821openalex-extracted
Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks
inferredcites →https://doi.org/10.1109/titb.2003.813794openalex-extracted
Computer-aided tumor detection in endoscopic video using color wavelet features
inferredcites →https://doi.org/10.1109/tmi.2009.2013851openalex-extracted
Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets
inferredcites →https://doi.org/10.1109/tmi.2014.2325607openalex-extracted
Comparative Validation of Single-Shot Optical Techniques for Laparoscopic 3-D Surface Reconstruction
inferredcites →https://doi.org/10.1109/tmi.2015.2434398openalex-extracted
Polyp Detection via Imbalanced Learning and Discriminative Feature Learning
inferredcites →https://doi.org/10.1109/tmi.2015.2487997openalex-extracted
Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information
inferredcites →https://doi.org/10.1111/j.1572-0241.2006.00390.xopenalex-extracted
Polyp Miss Rate Determined by Tandem Colonoscopy: A Systematic Review
inferredcites →https://doi.org/10.1117/12.2217148openalex-extracted
Colonoscopic polyp detection using convolutional neural networks
inferredcites →https://doi.org/10.1136/gut.52.suppl_4.iv7openalex-extracted
Magnification endoscopy, high resolution endoscopy, and chromoscopy; towards a better optical diagnosis
inferredcites →https://doi.org/10.1145/2647868.2654889openalex-extracted
Caffe
inferredcites →https://doi.org/10.1609/aaai.v30i1.10141openalex-extracted
Deep Contextual Networks for Neuronal Structure Segmentation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/mp.13865openalex-snapshot
Automated polyp segmentation for colonoscopy images: A method based on convolutional neural networks and ensemble learning
inferred← citeshttps://doi.org/10.1007/978-3-030-68793-9_23openalex-snapshot
DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
inferred← citeshttps://doi.org/10.1038/s41597-021-00882-2openalex-snapshot
Heidelberg colorectal data set for surgical data science in the sensor operating room
inferred← citeshttps://doi.org/10.1080/21681163.2020.1835546openalex-snapshot
VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery
inferred← citeshttps://doi.org/10.1109/isbi48211.2021.9433780openalex-snapshot
Colon10k: A Benchmark For Place Recognition In Colonoscopy
inferred← citeshttps://doi.org/10.48550/arxiv.2102.13644openalex-snapshot
Surgical Visual Domain Adaptation: Results from the MICCAI 2020 SurgVisDom Challenge
inferred← citeshttps://doi.org/10.48550/arxiv.2104.04832openalex-snapshot
Ensemble Learning based on Classifier Prediction Confidence and Comprehensive Learning Particle Swarm Optimisation for polyp localisation
inferred← citeshttps://doi.org/10.5445/ir/1000097120openalex-snapshot
Evaluation of Methods for Semantic Segmentation of Endoscopic Images
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