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Found 5 papers, 0 papers with code
EndoUDA: A modality independent segmentation approach for endoscopy imaging
While, today most segmentation approaches are supervised and only concentrated on a single modality dataset, this work exploits to use a target-independent unsupervised domain adaptation (UDA) technique that is capable to generalize to an unseen target modality.
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.
A deep learning framework for quality assessment and restoration in video endoscopy
Given the widespread use of endoscopy in different clinical applications, we contend that the robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem.
