World's Best Scientists 2026 revealed!
Christian Ledig

Christian Ledig

Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
33
Citations
22115
World Ranking
905
National Ranking
31

Computer Science

D-Index
30
Citations
23458
World Ranking
13798
National Ranking
664

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Christian Ledig is affiliated with the University of Bamberg in Germany. Their research spans multiple domains, primarily focusing on the intersection of computer science and medicine.

Their work covers the following main fields of study:

  • Computer Science
  • Medicine

Within these broad areas, Ledig contributes to several subfields including:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Radiology, Nuclear Medicine and Imaging
  • Cognitive Neuroscience
  • Biophysics

The main topics explored in their publications reflect an emphasis on medical and computational techniques, such as:

  • AI in cancer detection
  • Functional Brain Connectivity Studies
  • Cell Image Analysis Techniques
  • Artificial Intelligence in Healthcare and Education
  • Traumatic Brain Injury and Neurovascular Disturbances
  • Traumatic Brain Injury Research
  • Medical Image Segmentation Techniques

Recent papers authored or co-authored by Christian Ledig include:

  • "Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs", 2020, published in npj Digital Medicine
  • "Integrative Analysis of Circulating Metabolite Profiles and Magnetic Resonance Imaging Metrics in Patients with Traumatic Brain Injury", 2020, published in International Journal of Molecular Sciences
  • "Volume Change in Frontal Cholinergic Structures After Traumatic Brain Injury and Cognitive Outcome", 2020, published in Frontiers in Neurology
  • "Rethinking model prototyping through the MedMNIST+ dataset collection", 2025, published in Scientific Reports
  • "Differences Between MR Brain Region Segmentation Methods: Impact on Single-Subject Analysis", 2021, published in Frontiers in Big Data

Christian Ledig frequently publishes in the following venues:

  • arXiv (Cornell University)
  • npj Digital Medicine
  • International Journal of Molecular Sciences
  • Scientific Reports
  • Frontiers in Big Data

Collaboration is a significant aspect of their research, with frequent co-authors including:

  • Francesco Di Salvo
  • Sebastian Doerrich
  • Jussi P. Posti
  • Riikka Takala
  • Olli Tenovuo

Best Publications

  • Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

    Christian Ledig;Lucas Theis;Ferenc Huszar;Jose Caballero

  • Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

    Konstantinos Kamnitsas;Christian Ledig;Virginia F.J. Newcombe;Joanna P. Simpson

  • Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

    Christian Ledig;Lucas Theis;Ferenc Huszar;Jose Caballero

  • Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation

    Jose Caballero;Christian Ledig;Andrew Aitken;Alejandro Acosta

  • ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

    Oskar Maier;Bjoern H. Menze;Janina von der Gablentz;Levin Häni

  • Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

    Konstantinos Kamnitsas;Konstantinos Kamnitsas;Christian F. Baumgartner;Christian Ledig;Virginia F. J. Newcombe

  • Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain

    Antonios Makropoulos;Ioannis S Gousias;Christian Ledig;Paul Aljabar

  • Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

    Esther E. Bron;Marion Smits;Wiesje M. van der Flier;Hugo Vrenken

  • DeepMedic for Brain Tumor Segmentation

    Konstantinos Kamnitsas;Konstantinos Kamnitsas;Enzo Ferrante;Sarah Parisot;Christian Ledig

  • Cardiac image super-resolution with global correspondence using multi-atlas patchmatch.

    Wenzhe Shi;Jose Caballero;Christian Ledig;Xiahai Zhuang

  • Differential diagnosis of neurodegenerative diseases using structural MRI data

    Juha Koikkalainen;Hanneke Rhodius-Meester;Antti Tolonen;Frederik Barkhof

  • Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize

    Andrew P. Aitken;Christian Ledig;Lucas Theis;Jose Caballero

  • Robust whole-brain segmentation: Application to traumatic brain injury

    Christian Ledig;Rolf A. Heckemann;Rolf A. Heckemann;Alexander Hammers;Alexander Hammers;Juan Carlos Lopez

  • Multi-atlas segmentation with augmented features for cardiac MR images

    Wenjia Bai;Wenzhe Shi;Christian Ledig;Daniel Rueckert

  • A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease

    Tong Tong;Qinquan Gao;Ricardo Guerrero;Christian Ledig

  • Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database.

    Christian Ledig;Andreas Schuh;Ricardo Guerrero;Rolf A. Heckemann

  • Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI

    K Kamnitsas;L Chen;C Ledig;D Rueckert

  • Automatic quantification of normal cortical folding patterns from fetal brain MRI

    Robert Wright;Vanessa Kyriakopoulou;Christian Ledig;Mary A. Rutherford

  • Is the deconvolution layer the same as a convolutional layer

    Wenzhe Shi;Jose Caballero;Lucas Theis;Ferenc Huszar

  • Super resolution using a generative adversarial network

    Wenzhe Shi;Christian Ledig;Zehan Wang;Lucas Theis

  • Dynamic Changes in White Matter Abnormalities Correlate With Late Improvement and Deterioration Following TBI A Diffusion Tensor Imaging Study

    Virginia F. J. Newcombe;Marta M. Correia;Christian Ledig;Maria G. Abate

Frequent Co-Authors

Daniel Rueckert
Daniel Rueckert Technical University of Munich
Konstantinos Kamnitsas
Konstantinos Kamnitsas University of Oxford
Wenzhe Shi
Wenzhe Shi Twitter (United States)
Ben Glocker
Ben Glocker Imperial College London
Frederik Barkhof
Frederik Barkhof University College London
Joseph V. Hajnal
Joseph V. Hajnal King's College London
Alexander Hammers
Alexander Hammers King's College London
Wiesje M. van der Flier
Wiesje M. van der Flier Vrije Universiteit Amsterdam
Paul Aljabar
Paul Aljabar King's College London
Hilkka Soininen
Hilkka Soininen University of Eastern Finland

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online degrees in Computer Science gives you the flexibility to shape your education around your career goals. As technology evolves, Computer Science remains one of the best college majors for the future, opening doors to roles in software development, cybersecurity, AI, and data analytics.

If you’re considering further education after your bachelor's degree, there are easiest masters programs to get into that can help you fast-track career advancement, especially in IT management or computer systems analysis. For those aiming at academia or high-level research positions, pursuing one of the cheapest doctoral programs can make earning a PhD or EdD more accessible.

Fast and affordable options like online edd programs affordable can accelerate your journey into educational leadership or instructional technology. Whichever pathway you choose, online degrees can offer both affordability and flexibility while preparing you for future-ready careers.

Best Scientists Citing Christian Ledig

Trending Scientists

Recently Published Articles