A large fraction of the data gathered in health care consists of images and as the field transitions towards data-driven health, the opportunity for clinical decision support systems and thus the need for processing medical images becomes apparent. Deep learning artificial intelligence technologies have emerged as the state-of-the-art for image processing as they achieve unprecedented predictive performance due to their ability to learn complex representations from the input data. However, in safety-critical domains, such as the medical domain, there are key-obstacles that need to be resolved before wide spread adaption in the clinic. Obstacles for Universal Healthcare are the inability of deep learning approaches to provide interpretable solutions, the lack of theoretical understanding of deep learning models, and the lack of annotated patients in the health domain. MedEx will develop interpretable data-efficient methods that are able to use unannotated data and are applicable across hospitals and not limited to a particular imaging protocol. The primary application focus is the detection of lung-cancer from PET/MR/CT images in order to improve early-detection and thereby improve patient survivability and quality of care. The secondary application is diabetic retinopathy detection from fungus images to enable efficient systematic screening. While methodological advances are generic and will impact data-driven health and science beyond, concrete results and outcomes will find application through MedEx's clinical stakeholders. Despite being a high-risk endeavor, the project is feasible due to the combination of a high-quality team, top international collaborators, and direct involvement of clinical stakeholders.
Project leader: Michael Kampffmeyer
Institution: Institutt for fysikk og teknologi