Multi-class artefact detection in video endoscopy via convolution neural networks

This paper describes our approach for EAD2019: Multi-class artefact detection in video endoscopy. We optimized focal loss for dense object detection based RetinaNet network pretrained with the ImageNet dataset and applied several data augmentation and hyperparmeter tuning strategies, obtaining a weighted final score of 0.2880 for multi-class artefact detection task and mean average precision (mAP) score of 0.2187 with deviation 0.0770 for multi-class artefact generalisation task. In addition, we developed a U-Net based convolutional neural networks (CNNs) for multi-class artefact region segmentation task and achieved a final score of 0.4320 for the online test set in the competition.