Projects

This 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 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 structure-guided image registration.

An ultimate goal of our research is to develop computational methods for improving image-based detection and diagnosis of disease. We are developing machine learning tools for detection, segmentation and classification of lesions in neuro- and whole-body multi-modal imaging.

We actively develop new methodology and explore learning theory relevant to applications in medical imaging. Areas of research include meta-learning, multi-task & continual learning, causality, domain shift, geometric deep learning, semi-supervised and unsupervised learning, representation learning and Bayesian methods.

Robustness of machine learning based image analysis is one of the biggest challenges for bringing AI technology into clinical practice. Predictive performance can be impacted by changes in the data distribution due to scanner differences, data mismatch and population biases. In this project we develop methods for robust and reliable machine learning including automated quality control of image analysis tools.

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.