Georg Langs
 photo courtesy Adrian Dalca |
 MIT CSAIL 32 Vassar Street 32-D474 Cambridge, MA 02139 langs@csail.mit.edu twitter.com/georg
|
|
Publications
Last updated
|
I am a research affiliate with the Medical Vision Group at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT working with Polina Golland. I am also full professor of Machine Learning in Medical Imaging at Medical University of Vienna, where I am heading the Computational Imaging Research Lab.
My research interests are at the interface of machine learning and medical imaging. I am developing methods that help predicting disease course and treatment response, or to understand the development, change and evolution of the architecture of function and anatomy in the human brain. Lately I am very interested in understanding the association between phenotypes and underlying processes, and methods, we need to uncover them.
Selected recent publications Pan, J., Hofmanninger, J., Nenning, K.H., Prayer, F., Roehrich, S., Sverzellati, N., Poletti, V., Tomassetti, S., Weber, M., Prosch, H. and Langs, G., 2022. Unsupervised machine learning identifies predictive progression markers of IPF. European Radiology, pp.1-11. paper | pdf Nenning, K.H. and Langs, G., 2022. Machine learning in neuroimaging: from research to clinical practice. Die Radiologie, pp.1-10. paper | pdf Sobotka, D., Ebner, M., Schwartz, E., Nenning, K.H., Taymourtash, A., Vercauteren, T., Ourselin, S., Kasprian, G., Prayer, D., Langs, G. and Licandro, R., 2022. Motion correction and volumetric reconstruction for fetal functional magnetic resonance imaging data. NeuroImage, 255, p.119213. paper Roehrich, S., Heidinger, B.H., Prayer, F., Weber, M., Krenn, M., Zhang, R., Sufana, J., Scheithe, J., Kanbur, I., Korajac, A. and Poetsch, N., 2022. Impact of a content-based image retrieval system on the interpretation of chest CTs of patients with diffuse parenchymal lung disease. European Radiology, pp.1-8. paper | pdf Burger, B., Nenning, K.H., Schwartz, E., Margulies, D.S., Goulas, A., Liu, H., Neubauer, S., Dauwels, J., Prayer, D. and Langs, G., 2022. Disentangling cortical functional connectivity strength and topography reveals divergent roles of genes and environment. Neuroimage, 247, p.118770. paper Perkonigg, M., Hofmanninger, J., Herold, C.J., Brink, J.A., Pianykh, O., Prosch, H. and Langs, G., 2021. Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging. Nature Communications, 12(1), pp.1-12. paper Schlegl, T., Seeboeck, P., Waldstein, S.M., Schmidt-Erfurth, U. and Langs, G., 2017, June. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In International conference on information processing in medical imaging (pp. 146-157). Springer, Cham. pdf
|
