World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
20019
World Ranking
6292
National Ranking
46

Overview

Georg Langs is affiliated with the Medical University of Vienna in Austria. Their primary field of study is Medicine, with a significant focus on Radiology, Nuclear Medicine and Imaging.

The scientist's research spans multiple subfields, including:

  • Radiology, Nuclear Medicine and Imaging
  • Artificial Intelligence
  • Pediatrics, Perinatology and Child Health
  • Cognitive Neuroscience
  • Pulmonary and Respiratory Medicine

Langs' main research topics encompass various advanced domains such as:

  • Radiomics and Machine Learning in Medical Imaging
  • Advanced Neuroimaging Techniques and Applications
  • Neonatal and fetal brain pathology
  • Functional Brain Connectivity Studies
  • Fetal and Pediatric Neurological Disorders
  • Domain Adaptation and Few-Shot Learning
  • AI in cancer detection

Their recent papers reflect a broad engagement with both methodological and applied research. Notable publications include:

  • "Introduction to Radiomics" (2020), published in Journal of Nuclear Medicine
  • "Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans" (2020), published in Research Portal (King's College London)
  • "BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets" (2020), published in Communications Biology
  • "Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem" (2020), published in European Radiology Experimental
  • "Cross-species functional alignment reveals evolutionary hierarchy within the connectome" (2020), published in NeuroImage

Langs collaborates frequently with a group of coauthors, including:

  • Karl-Heinz Nenning
  • Gregor Kasprian
  • Daniela Prayer
  • Helmut Prosch
  • Ernst Schwartz

The scientist's contributions are often published in select venues such as:

  • arXiv (Cornell University)
  • Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
  • European Radiology
  • NeuroImage
  • Cerebral Cortex

Best Publications

  • Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

    Thomas Schlegl;Philipp Seeböck;Sebastian M. Waldstein;Ursula Schmidt-Erfurth

  • Situating the default-mode network along a principal gradient of macroscale cortical organization

    Daniel S. Margulies;Satrajit S. Ghosh;Satrajit S. Ghosh;Alexandros Goulas;Marcel Falkiewicz

  • Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

    Michael Roberts;Michael Roberts;Derek Driggs;Matthew Thorpe;Julian D. Gilbey

  • Causability and explainability of artificial intelligence in medicine.

    Andreas Holzinger;Georg Langs;Helmut Denk;Kurt Zatloukal

  • Introduction to Radiomics

    Marius E. Mayerhoefer;Marius E. Mayerhoefer;Andrzej Materka;Georg Langs;Ida Häggström

  • f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.

    Thomas Schlegl;Philipp Seeböck;Sebastian M. Waldstein;Georg Langs

  • BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

    Reinder Vos de Wael;Oualid Benkarim;Casey Paquola;Sara Lariviere

  • Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning

    Thomas Schlegl;Sebastian M. Waldstein;Hrvoje Bogunovic;Franz Endstraßer

  • Parcellating cortical functional networks in individuals

    Danhong Wang;Randy L Buckner;Michael D Fox;Michael D Fox;Daphne J Holt

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

    Johannes Hofmanninger;Florian Prayer;Jeanny Pan;Sebastian Rohrich

  • The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space

    Johanna Klughammer;Barbara Kiesel;Thomas Roetzer;Nikolaus Fortelny

  • Cross-species functional alignment reveals evolutionary hierarchy within the connectome.

    Ting Xu;Karl Heinz Nenning;Ernst Schwartz;Seok Jun Hong

  • Fast Active Appearance Model Search Using Canonical Correlation Analysis

    R. Donner;M. Reiter;G. Langs;P. Peloschek

  • Continuous Learning AI in Radiology: Implementation Principles and Early Applications.

    Oleg S. Pianykh;Georg Langs;Marc Dewey;Dieter R. Enzmann

  • Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks

    Oscar Jimenez-del-Toro;Henning Muller;Markus Krenn;Katharina Gruenberg

  • Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach.

    Hrvoje Bogunovic;Sebastian M Waldstein;Thomas Schlegl;Georg Langs

  • Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration.

    Ursula Schmidt-Erfurth;Hrvoje Bogunovic;Amir Sadeghipour;Thomas Schlegl

  • Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT

    Philipp Seebock;Jose Ignacio Orlando;Thomas Schlegl;Sebastian M. Waldstein

  • Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion

    Georg Langs;Georg Langs;Polina Golland;Satrajit S. Ghosh;Satrajit S. Ghosh

  • Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data

    Philipp Seebock;Sebastian M. Waldstein;Sophie Klimscha;Hrvoje Bogunovic

  • Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks

    Thomas Schlegl;Sebastian M. Waldstein;Wolf-Dieter Vogl;Ursula Schmidt-Erfurth

Frequent Co-Authors

Henning Müller
Henning Müller University of Applied Sciences and Arts Western Switzerland
Bjoern H. Menze
Bjoern H. Menze University of Zurich
Horst Bischof
Horst Bischof Graz University of Technology
Nikos Paragios
Nikos Paragios CentraleSupélec
Peter Brugger
Peter Brugger University of Zurich
Veronika Schöpf
Veronika Schöpf University of Graz
Zhuowen Tu
Zhuowen Tu University of California, San Diego
Hesheng Liu
Hesheng Liu Peking University

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