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Found 8 papers, 1 papers with code
Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction
This contribution shows how an appropriate image pre-processing can improve a deep-learning based 3D reconstruction of colon parts.
Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging
Endoscopy is the most widely used imaging technique for the diagnosis of cancerous lesions in hollow organs.
A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models
Endoscopy is the most widely used medical technique for cancer and polyp detection inside hollow organs.
Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams
Extensive experiments show the superiority, in terms of mean average precision, of the ensemble approach over the individual models and previous works in the state of the art.
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?
