| Vol-2366 urn:nbn:de:0074-2366-4 Copyright © JJJJ for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

# EAD2019
EAD Challenge: Multi-class Artefact Detection in Video Endoscopy

### Proceedings of the 2019 Challenge on Endoscopy Artefacts Detection (EAD2019)

### co-located with the 16th International Symposium on Biomedical Imaging (ISBI)

Edited by

### Sharib Ali *, **
Felix Zhou***

* Department of Engineering Science, Institute of Biomedical Engineering, Old Road, University of Oxford, Oxford, UK
** Big Data Institute, Old Road, University of Oxford, Oxford, UK
*** Ludwig Institute for Cancer Research, Oxford Branch, University of Oxford, Oxford, UK

## Table of Contents

- Preface

- Endoscopic Artefact Detection And Segmentation With Deep Convolutional Neural Network
  Suhui Yang, Guanju Cheng
- Artefact Detection In Video Endoscopy Using Retinanet And Focal Loss Function
  Ilkay Oksuz, James R. Clough, Andrew P. King, Julia A. Schnabel
- Ensemble Mask-aided R-CNN
  Pengyi Zhang, Xiaoqiong Li, YunXin Zhong
- Detect Artefacts Of Various Sizes On The Right Scale For Each Class In Video Endoscopy
  Xiaokang Wang, Chunqing Wang
- Focal Loss For Artefact Detection In Medical Endoscopy
  Maxime Kayser, Roger D. Soberanis-Mukul, Shadi Albarqouni, Nassir Navab
- Graph-search Based UNet-d For The Analysis Of Endoscopic Images
  Shufan Yang, Sandy Cochran
- DNN Models And Postprocessing Thresholds For Endoscopy Artifact Detection In Practice
  Seiryo Watanabe, Shigeto Seno, Hideo Matsuda
- Deep Layer Aggregation Approaches For Region Segmentation Of Endoscopic Images
  Qingtian Ning, Xu Zhao, Jingyi Wang
- Multi-class Artefact Detection In Video Endoscopy Via Convolution Neural Networks
  Mohammad Azam Khan, Jaegul Choo
- Patch-based Deep Learning Approaches For Artefact Detection Of Endoscopic Images
  Xiaohong W. Gao, Yu Qian

2019-05-22: submitted by Sharib Ali, metadata incl. bibliographic data published under Creative Commons CC0
2019-05-dd: published on CEUR-WS.org |valid HTML5|