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Found 5 papers, 1 papers with code
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy.
A multi-centre polyp detection and segmentation dataset for generalisability assessment
To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as \textit{PolypGen}) curated by a team of computational scientists and expert gastroenterologists.
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies.
Endoscopy disease detection challenge 2020
What could be more important than disease detection and localization?
Endoscopy artifact detection (EAD 2019) challenge dataset
Endoscopic artifacts are a core challenge in facilitating the diagnosis and treatment of diseases in hollow organs.
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