@misc{14903,
  author = {Konstantin Pogorelov and Olga Ostroukhova and Andreas Petlund and P{\r a}l Halvorsen and H{\r a}vard Espeland and Tomas Kupka and Thomas de Lange and Carsten Griwodz and Michael Riegler},
  title = {Automatic Detection of Angiectasia: Evaluation of Deep Learning and Handcrafted Approaches},
  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.},
  year = {2018},
  journal = {IEEE Conference on Biomedical and Health Informatics (BHI) 2018},
}