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.

通过 2019 年内窥镜伪影检测（EAD）挑战，解决内镜图像帧的伪影定位和鉴定问题，从而保证了可以进一步开发用于患者护理的重要的定量分析工具，如拼接和 3D 重建。