Objectives and Scientific Topics

There has been an increasing interest of the MICCAI community for data-driven methods such as supervised learning techniques. The effectiveness of such approaches often depends on their access to sufficiently large quantities of labelled data of good quality. Despite the increasing amount of acquired clinical data, the availability of ready-to-use annotations is very limited. A first purpose of this workshop is to raise awareness on the importance of a methodological acquisition of training data and a careful design of the labelling procedures. A second goal is therefore, to promote the development and scientific exchange of algorithms that focus on assisting the annotation process by making it for example more general, more accurate, faster or more intuitive for the medical experts. To this end, we will include in the program an introductory keynote speech, while calling for paper submissions addressing the labelling/annotation task by means of approaches from the following fields:

  • Active learning
  • Semi-supervised learning
  • Reinforcement learning
  • Domain adaptation and transfer learning
  • Crowd-sourcing annotations and fusion of labels from different sources
  • Data augmentation
  • Modelling of label uncertainty
  • Visualization and human-computer interaction

Because data annotation and expert labelling is strongly grounded in practical considerations, we welcome not only research contributions, but also encourage submitters to share war stories and practical feedback on successful or insightfully unsuccessful data collection experiences in real world settings