WS05 - Workshop on Medical image and data analysis with deep learning algorithms

Workshop Organized by

Anisia Culea Florescu, Dunarea de Jos University of Galati, Romania and Lucian Mihai Itu, Transilvania University of Brasov, Romania and Simona Moldovanu, Dunarea de Jos University of Galati, Romania

Aims and Objectives

In recent years, deep learning, as part of artificial intelligent, is increasingly used in medical imaging where it has come to play a key role in assisted diagnosis, planning, monitoring, and evaluation of the treatment. The types of medical images that can feed a deep learning algorithm are focused on mammography, dermoscopy, ultrasounds, tactile imaging, computerized tomography, magnetic resonance imaging, endoscopy or X-rays. Medical images are complex, and due to their specific acquisition process some details cannot be very clear and easy to interpret. Consequently, the medical and engineering specialists have come to work closely to develop new methods for providing a diagnosis as quickly and accurately as possible.

Topics under this workshop include (but not limited to)

  • Medical images
  • Feature extraction
  • Segmentation
  • Classification
  • Registration
  • Computer aided detection
  • Landmark detection
  • Image or view recognition
  • Multi-task learning
  • Transfer learning
  • Generative learning
  • Self-supervised learning
  • Weakly supervised learning
  • Unsupervised learning
  • Privacy preserving learning
  • Explainability and interpretability
  • Robustness and out-of-distribution detection
  • Uncertainty quantification

Download Call for Papers

Click here to download the workshop cfp.

Contact for more details

If you would like to know more about the workshop, please contact, Anisia Culea Florescu ([email protected]); Lucian Mihai Itu ([email protected]); Simona Moldovanu ([email protected]);