Projects

This ERC funded project is devoted to redefine the state-of-the-art in medical image analysis by developing a new generation of machine intelligence using powerful techniques of representation learning. An overarching objective is to harvest information from population data to construct advanced statistical models of anatomy.

We actively develop new methodology and explore learning theory relevant to applications in medical imaging. Areas of research include causality, domain shift, robustness & reliability, bias & fairness, generative modelling, semi-supervised & self-supervised learning, deep representation learning, and geometric deep learning.

Our work on medical imaging AI is increasingly contributing to health policy and AI regulation. We have contributed to several recommendations and guidelines on how to evaluate, test and audit AI systems. Our work focuses on monitoring and assessment of bias and fairness, aiming to assure safe and ethical deployment of AI in clinical practice.<br /> <br />

The ultimate goal of our research is to build safe and ethical computational tools for improving image-based detection and diagnosis of disease. We are actively developing new algorithms for object detection, image segmentation and classification with applications in diagnosis of cardiovascular disease, detection of breast cancer, radiotherapy planning, quantification of brain lesions, and many more.

We develop advanced image analysis algorithms for extracting clinically useful information from raw medical image data. This is what we call Semantic Imaging. Applications include multi-modal segmentation, anatomy recognition, object localization, image classification and learning-based image registration.

We bring together advanced computer vision methods such as dense 3D real-time reconstruction and machine learning based image analysis for applications in medical augmented reality.