@inbook{16193,
  keywords = {Performance, Anomaly detection, Semantic segmentation, artificial intelligence, Gastrointestinal endoscopy, Neural Networks, Hand-crafted features},
  author = {Inga Str{\"u}mke and Steven Hicks and Vajira Thambawita and Debesh Jha and Sravanthi Parasa and Michael Riegler and P{\r a}l Halvorsen},
  title = {Artificial Intelligence in Gastroenterology},
  abstract = {The holy grail in endoscopy examinations has for a long time been assisted diagnosis using Artificial Intelligence (AI). Recent developments in computer hardware are now enabling technology to equip clinicians with promising tools for computer-assisted diagnosis (CAD) systems. However, creating viable models or architectures, training them, and assessing their ability to diagnose at a human level, are complicated tasks. This is currently an active area of research, and many promising methods have been proposed. In this chapter, we give an overview of the topic. This includes a description of current medical challenges followed by a description of the most commonly used methods in the field. We also present example results from research targeting some of these challenges, and a discussion on open issues and ongoing work is provided. Hopefully, this will inspire and enable readers to future develop CAD systems for gastroenterology.},
  year = {2021},
  journal = {Artificial Intelligence in Medicine},
  pages = {1 - 20},
  month = {09/2021},
  publisher = {Springer International Publishing},
  address = {Cham},
  isbn = {978-3-030-58080-3},
  url = {https://link.springer.com/referenceworkentry/10.1007\%2F978-3-030-58080-3_163-2},
  doi = {10.1007/978-3-030-58080-3_163-2},
  editor = {Niklas Lidstr{\"o}mer and Hutan Ashrafian},
}