@misc{14902,
  keywords = {Machine learning, Angiectasia, computer aided diagnosis, deep learning, video capsular endoscopy},
  author = {Konstantin Pogorelov and Olga Ostroukhova and Andreas Petlund and P{\r a}l Halvorsen and Thomas de Lange and H{\r a}vard Espeland and Tomas Kupka and Carsten Griwodz and Michael Riegler},
  title = {Deep Learning and Handcrafted Feature Based Approaches for Automatic Detection of Angiectasia},
  abstract = {Angiectasia, formerly called angiodysplasia, is one of the most frequent vascular lesions and often the cause of gastrointestinal bleedings. Medical specialists assessing videos or images of examinations reach a detection performance of 16\% for the detection of bleeding to 69\% for the detection of angiectasia. This shows that automatic detection to support medical experts can be useful. In this paper, we present several machine learning-based approaches for angiectasia detection in wireless video capsule endoscopy frames. In summary, the most promising results for pixel-wise localization and framewise detection are obtained by the proposed deep learning method using generative adversarial networks (GANs). Using this approach, we achieve 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. Thus, the results demonstrate the capability of using deep learning for automatic angiectasia detection in real clinical settings.},
  year = {2018},
  journal = {2018 IEEE Conference on Biomedical and Health Informatics (BHI)},
  pages = {365-368},
  publisher = {IEEE},
  doi = {10.1109/BHI.2018.8333444},
}