Authors | K. Pogorelov, O. Ostroukhova, A. Petlund, P. Halvorsen, H. Espeland, T. Kupka, T. de Lange, C. Griwodz and M. Riegler |
Title | Automatic Detection of Angiectasia: Evaluation of Deep Learning and Handcrafted Approaches |
Afilliation | Communication Systems |
Project(s) | Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources, Department of Holistic Systems |
Status | Published |
Publication Type | Talk, keynote |
Year of Publication | 2018 |
Location of Talk | IEEE Conference on Biomedical and Health Informatics (BHI) 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. |
Citation Key | 25795 |