We improve the management of prostate cancer patients by developing machine learning-based prognostic models that stratify patients into those that will benefit from additional diagnostic procedures and those that do not.
We develop novel techniques for quantitative analysis of medical images, with a focus on deep learning techniques and on large-scale image-based studies. Currently our main application areas are in neuro-, vascular-, and pulmonary image analysis
We bring state-of-the-art techniques from computer science to the medical imaging domain, further developing, optimizing and rigorously validating them.
We develop novel techniques to co-learn from various datatypes using AI to perform integrated diagnostics, focussed on creating imaging-based biomarkers to improve diagnosis and treatment in oncology.
Our infrastructure can greatly benefit personalized medicine by making pipelines for imaging biomarker extraction available to researchers and clinicians. Additionally, we create a reference database for different imaging biomarkers, which can be used to compare an individual against the general population. This will enable improved re-use of imaging data for diagnostics and prognostics.
Our goals are to improve the understanding of how various omics affect the complex traits and to make use of such insights to improve the diagnosis, prevention and treatment of diseases whenever possible.
We develop accurate and robust tools for clinicians, such as retinal motion correction and co-localization of different imaging modalities, by using artificial intelligence and image processing techniques on ophthalmic data.
We are developing trackerless navigation approaches, where the imaging systems used for image guidance are utilized to track the anatomy and instruments, and to align pre-operative 3D models to the interventional images.
We aim to optimally combine brain imaging, clinical data and artificial intelligence techniques to promote an accurate and early diagnosis, and eventually the right treatment, for patients with neurodegenerative disease.
We aim to design accelerated quantitative MRI acquisitions and use advanced reconstruction techniques to create images that allow clinical diagnosis to be made.