@inproceedings{8650668,
  abstract     = {{Today, hospitals are producing a staggering amount of digital information, stored into electronic health records. The major volume of healthcare data comes from medical imaging. Due to advances in medical image acquisition, novel imaging procedures are introduced, and the amount of diagnostic imaging is rapidly increasing. Analysing all the images has become a tremendous challenge and a bottleneck for efficient diagnosis, therapy planning and follow-up. At the same time, these large amounts of data also provide opportunities to develop computer-aided image analysis tools that will become indispensable to efficiently extract relevant information. For example, in image segmentation where objects of interest are detected and delineated, Artificial Intelligence (AI) can play an important role. Manual delineations by human experts are tedious and time-consuming and thus impractical in clinical routine, but also suffer from inter- and intra-observer variability. AI and more specifically deep learning algorithms like convolutional neural networks, can perform image segmentation in an automatic and more efficient way. Besides segmentation, AI is being applied to numerous image analysis tasks like image reconstruction, registration, classification, imaging genomics etc. Several challenges remain to be tackled to ensure adoption of AI in clinical routine. Despite the fact that the amount of recorded data is increasing fast, it is scattered across different independent centres with large variations in protocols. Furthermore, the amount of data in imaging remains small in comparison to datasets found in other non-medical computer vision domains. Finally, more research is needed towards explainable AI to understand and trust the developed algorithms.}},
  author       = {{Decuyper, Milan and Van Holen, Roel}},
  booktitle    = {{CRIG Industrial Partnering Event 2019, Abstracts}},
  keywords     = {{Artificial Intelligence,Deep Learning,Medical Imaging}},
  language     = {{eng}},
  location     = {{Ghent, Belgium}},
  title        = {{AI in medical imaging}},
  url          = {{https://www.crig.ugent.be/en/events/crigs-2nd-industrial-partnering-event}},
  year         = {{2019}},
}