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About
Georg Langs is a leading researcher in medical image analysis and computational anatomy, with a focus on developing machine learning methods for understanding complex biomedical imaging data. His work bridges computer vision and clinical applications, particularly in the analysis of retinal and brain imaging. Langs has made significant contributions to 2D/3D image registration, including his recent work on rotation-equivariant feature matching for rigid single-slice-in-volume registration, which enables robust alignment of sparse medical images with volumetric data. His research has been widely cited, with his most influential papers accumulating hundreds of citations, reflecting his impact on fields such as ophthalmology and neurology. Langs is also known for his work on predicting disease progression from imaging biomarkers, and he has been instrumental in advancing explainable AI for medical imaging. He leads the Computational Imaging Research Lab at the Medical University of Vienna, where his team develops algorithms that improve diagnostic accuracy and treatment planning. His contributions have been recognized with multiple awards, and he actively collaborates on large-scale European research initiatives.
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