Georg Langs - research


Functional Geometry of the Brain

We are exploring a functional geometry of cognitive processes. This geometry captures the global interaction pattern within the brain by mapping it to a space so that proximity reflects the functional relation. The map is obtained from fMRI data based on a diffusion process defined on the set of BOLD signals. It establishes a means of exploring the entirety of functional interactions, and the mutual roles of individual regions during particular tasks or conditions.

Autonomous Model Learning

The autonomous building of models and the learning of structures and behavior from un-annotated data is a way to cope with the vast and growing amount of medical imaging modalities, and with the rich information they provide. Autonomous learning might be a key to this data, and could enable clinicians to utilize and explore the information in an intuitive manner.

Shape Maps

Shape maps are based on diffusion maps, and the minimum description length principle. They map a set of training shape examples to a metric space - the map - where the Euclidean distance relates to the joint modeling behavior of landmarks. That is, after running shape maps on your data, every landmark is mapped to a position so that if two landmarks are close they can be modelled cheaply with the same model. This gives a notion of coherenence for sub-sets of your data.

Rheumatoid Arthritis Quantification

We work on the automatic quantification of erosion development and joint space narrowing during the course of rheumatoid arthritis (RA). The work is aimed at a more precise monitoring of RA during therapy and during multi center clinical trials. This was my main PhD application work together with the Medical University Vienna in the ongoing AAMIR project.