Parallels of medical imaging and natural language processing
Machine learning is used extensively in different applications, including medical imaging and natural language processing. As different types of data are involved, it is reasonable to assume that different methods are needed for each application. However, there are also opportunities in translating a method successful in one application, to the other application where it is not widely used.
The goal of this project is to examine trends in recent conference papers for both fields, and identify relevant similarities/differences between the approaches used. Multiple subprojects (for different students) are available, focusing on: * Pretraining on public datasets * Multi-task learning * Self-supervised learning * (Other subjects can be selected in discussion with the student)
The projects are mainly set up as literature reviews. However, depending on your interests you could additionally use (machine learning) tools for automating paper or citation analysis.
The projects will be supervised jointly by Veronika Cheplygina and Barbara Plank.