Speaker
Description
Machine learning has vast potential in medical image analysis, improving possibilities for early diagnosis and prognosis of disease. Algorithms typically need large amounts of representative, annotated examples for good performance, which may be difficult to achieve, for example due to differences between image acquisition procedures, or the time and effort involved in annotation. To address these problems, several approaches have been proposed, which are either aimed at adapting to use other types of annotated data, and or at gathering annotations more efficiently. In this talk I will highlight two such approaches: transfer learning from natural images such as cats, and crowdsourcing by annotators without medical expertise. I will also discuss more general issues we face we as a community face when addressing such problems.
| Speaker's Affiliation | IT University of Copenhagen |
|---|---|
| Member State or IGO/NGO | Denmark |
