Artificial Intelligence driven Biomedical Imaging Innovation

Wider research context: Imaging data is of main relevance for biomedical research. A challenge is the huge amount of data generated and the need for unbiased quantitation. Despite progress in automated image analysis, the interpretation of images and videos often requires manual tasks and scoring by specialists, which is time-consuming and subjective. This is particularly true for explorative studies, where the relevant readout is not strictly defined at the onset.
Objectives and hypotheses: The proposed doctoral program addresses challenges in biomedical image analysis in the areas of paraplegia, infectious diseases and cellular communication using machine vision and learning techniques, including artificial intelligence. Computer and biomedical scientists will work together for an iterative optimization of how imaging data are acquired and analyzed. This multidisciplinary approach will lead to innovative solutions and to the education of scientists who are well prepared for a career in this highly dynamic field.
Approach: The PhD-students will work on interlinked projects that rely on imaging data, varying from 2- and 3-dimensional microscopic to magnetic resonance imaging. Either the biological or the computer vision and learning parts are at the core of each PhD-project. Through a close collaboration within the PhD-projects and by common lectures, seminars and journal clubs, a peer-group will be formed that will generate a model for interdisciplinary collaborations between the institutions.