Georg Langs

Full Professor of Machine Learning in Medical Imaging

Medical University of Vienna

Austria

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Georg Langs is a Full Professor of Machine Learning in Medical Imaging at the Medical University of Vienna, Austria. His research focuses on the application of artificial intelligence in various medical imaging contexts, including liver disease, neurometabolic imaging, and respiratory pandemics. He has contributed to advancements in imaging techniques and predictive models for patient outcomes, particularly in the areas of preterm neonates and oncologic imaging.

Positions (1)

Georg Langs

University Name

Medical University of Vienna

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AI and Medicine

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Georg Langs

Research Interests

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About

Positions (1)

AI and Medicine

Articles (19)

Identifying signatures of image phenotypes to track treatment response in liver disease

Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma

Integrated Diagnostics: Embracing Artificial Intelligence and, Above All, Multidisciplinary Teamwork

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training

Contrast Agent Dynamics Determine Radiomics Profiles in Oncologic Imaging

Artificial intelligence in medical imaging is a tool for clinical routine and scientific discovery

Artificial intelligence in respiratory pandemics—ready for disease X? A scoping review

Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage

Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study

Innovations in Deep Learning to Predict Individual Risk and Treatment Outcome

Evolution of cortical geometry and its link to function, behaviour and ecology

Fetal temporal sulcus depth asymmetry has prognostic value for language development

Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics

Radiomics score predicts acute respiratory distress syndrome based on the initial CT scan after trauma

Correction to: Radiomics score predicts acute respiratory distress syndrome based on the initial CT scan after trauma

Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging

Analyzing Longitudinal wb-MRI Data and Clinical Course in a Cohort of Former Smoldering Multiple Myeloma Patients: Connections between MRI Findings and Clinical Progression Patterns

Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem

Collaborators (14)

Hedvig Hricak

Nathan Jeffery

Priska Bauerstätter

Bernhard Baumann

Adelheid Woehrer

Thomas Roetzer-Pejrimovsky

Lisa Bartha-Doering

Martin Rajchl

Benedikt Heidinger

Wolfgang Bogner

Helmut Prosch

Marlene Stuempflen

Johanna Klughammer

Pascal A. T. Baltzer

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