@misc {25795,
	title = {Automatic Detection of Angiectasia: Evaluation of Deep Learning and Handcrafted Approaches},
	howpublished = { IEEE Conference on Biomedical and Health Informatics (BHI) 2018},
	year = {2018},
	abstract = {Angiectasia, formerly called angiodysplasia, is one of the most frequent vascular lesions and often the cause of gastrointestinal bleedings. Medical specialists assessing videos of examinations reach a detection performance of 16\% for the detection of bleeding to 69\% for the detection of angiectasia. In this paper, we present several machine-learning-based approaches for angiectasia detection in wireless video capsule endoscopy images. The most promising results for pixel-wise localization and frame-wise detection are obtained by the proposed deep learning approach using generative adversarial networks (GANs) with a sensitivity of 88\% and specificity of 99.9\% for pixel-wise localization and a sensitivity of 98\% and a specificity of 100\% for frame-wise detection, which fits the requirements for automatic angiectasia detection in real clinical settings.},
	author = {Pogorelov, Konstantin and Ostroukhova, Olga and Petlund, Andreas and Halvorsen, P{\r a}l and Espeland, H{\r a}vard and Kupka, Tomas and de Lange, Thomas and Griwodz, Carsten and Riegler, Michael}
}
