Endoscopy artifact detection (EAD 2019) challenge dataset

@article{Ali2019EndoscopyAD,
  title={Endoscopy artifact detection (EAD 2019) challenge dataset},
  author={Sharib Ali and Felix Y Zhou and Christian Daul and Barbara Braden and Adam Bailey and Stefano Realdon and James Edward East and Georges Wagni{\`e}res and Victor Loschenov and Enrico Grisan and Walter Blondel and J. Rittscher},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.03209}
}
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… 
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50 Citations
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50 Citations

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TLDR
The paper briefly describes the methodology adopted for the 2020 Endoscopy Artefact Detection and Segmentation (EAD2020) challenge, and how three off-theshelf deep detection networks have been optimised and evaluated on the EAD data.
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TLDR
Experimental results show that the proposed objection detectors based on SSD and Faster-RCNN significantly outperform the baseline and improve the performance of two improved CNN-based methods for seven-class endoscopic artifact detection (EAD).
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An endoscopic artefact detection framework based on an ensemble of deep neural networks, classagnostic non-maximum suppression, and false-positive elimination is proposed, exploiting the distinctive properties of different approaches.
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This work proposed to combine the advantages of two state-of-the-art deep object detection algorithms, YOLOv3 and Mask R-CNN, and achieved improved performance by leveraging the available image segmentation annotations to aid bounding box detection.
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