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Endoscopy artifact detection (EAD 2019) challenge dataset

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arxiv 1905.03209 v1 pith:RWNWZJTC submitted 2019-05-08 cs.CV cs.AIcs.LGeess.IV

Sharib Ali , Felix Zhou , Christian Daul , Barbara Braden , Adam Bailey , Stefano Realdon , James East , Georges Wagnières
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Victor Loschenov Enrico Grisan Walter Blondel Jens Rittscher
This is my paper
classification cs.CVcs.AIcs.LGeess.IV
keywords challengeartifactsdetectionendoscopicartifactcarecrucialdataset
verification ladder T0 review T1 audit T2 compute T3 formal
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Endoscopic artifacts are a core challenge in facilitating the diagnosis and treatment of diseases in hollow organs. Precise detection of specific artifacts like pixel saturations, motion blur, specular reflections, bubbles and debris is essential for high-quality frame restoration and is crucial for realizing reliable computer-assisted tools for improved patient care. At present most videos in endoscopy are currently not analyzed due to the abundant presence of multi-class artifacts in video frames. Through the endoscopic artifact detection (EAD 2019) challenge, we address this key bottleneck problem by solving the accurate identification and localization of endoscopic frame artifacts to enable further key quantitative analysis of unusable video frames such as mosaicking and 3D reconstruction which is crucial for delivering improved patient care. This paper summarizes the challenge tasks and describes the dataset and evaluation criteria established in the EAD 2019 challenge.

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