World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
8549
World Ranking
8308
National Ranking
192

Overview

Su Ruan is affiliated with the University of Rouen in France and has a research focus primarily in computer science and medicine. Their work spans multiple subfields including computer vision and pattern recognition, radiology, nuclear medicine and imaging, artificial intelligence, neurology, and media technology.

Their main topics of research include:

  • Radiomics and Machine Learning in Medical Imaging
  • Brain Tumor Detection and Classification
  • Medical Image Segmentation Techniques
  • Advanced Neural Network Applications
  • Medical Imaging Techniques and Applications
  • AI in Cancer Detection
  • Generative Adversarial Networks and Image Synthesis

Su Ruan has published extensively, including several recent papers such as:

  • Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation (2020), Computers in Biology and Medicine
  • Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities (2021), IEEE Transactions on Image Processing
  • Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review (2023), Journal of Imaging
  • AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging (2021), PET Clinics
  • Lymphoma segmentation from 3D PET-CT images using a deep evidential network (2022), International Journal of Approximate Reasoning

Frequent collaborators in Su Ruan's research include Pierre Véra, Pierre Decazes, Ling Huang, Romain Modzelewski, and Tongxue Zhou.

Their work has been published in several venues repeatedly, with notable recurring publication outlets including:

  • arXiv (Cornell University)
  • Computerized Medical Imaging and Graphics
  • Computers in Biology and Medicine
  • Neurocomputing
  • SSRN Electronic Journal

The combination of their research interests and frequent publication venues highlights a focus on machine learning and AI applications in medical imaging and analysis.

Best Publications

  • Medical Image Synthesis with Context-Aware Generative Adversarial Networks

    Dong Nie;Roger Trullo;Jun Lian;Caroline Petitjean

  • A review: Deep learning for medical image segmentation using multi-modality fusion

    Tongxue Zhou;Tongxue Zhou;Su Ruan;Stéphane Canu

  • Medical Image Synthesis with Deep Convolutional Adversarial Networks

    Dong Nie;Roger Trullo;Jun Lian;Li Wang

  • Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation.

    Amine Amyar;Amine Amyar;Romain Modzelewski;Hua Li;Su Ruan

  • Right ventricle segmentation from cardiac MRI: a collation study.

    Caroline Petitjean;Maria A. Zuluaga;Wenjia Bai;Jean Nicolas Dacher

  • A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images

    Weibei Dou;Su Ruan;Yanping Chen;Daniel Bloyet

  • Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation

    Nan Zhang;Su Ruan;Stéphane Lebonvallet;Qingmin Liao

  • Brain tissue classification of magnetic resonance images using partial volume modeling

    S. Ruan;C. Jaggi;J. Xue;J. Fadili

  • Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities

    Tongxue Zhou;Stephane Canu;Pierre Vera;Su Ruan

  • An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism

    Tongxue Zhou;Stéphane Canu;Su Ruan

  • A robust agorithm for eye detection on gray intensity face without spectacles

    Kun Peng;Liming Chen;Su Ruan;Georgy Kukharev

  • A multistep unsupervised fuzzy clustering analysis of fMRI time series.

    M.J. Fadili;S. Ruan;D. Bloyet;B. Mazoyer

  • Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions

    Chunfeng Lian;Su Ruan;Thierry Denoeux;Hua Li

  • Fuzzy Markovian segmentation in application of magnetic resonance images

    Su Ruan;Bruno Moretti;Jalal Fadili;Daniel Bloyet

  • Graph cut segmentation with a statistical shape model in cardiac MRI

    D. Grosgeorge;C. Petitjean;J. N. Dacher;S. Ruan

  • On the number of clusters and the fuzziness index for unsupervised FCA application to BOLD fMRI time series

    Mohamed-Jalal Fadili;Su Ruan;Daniel Bloyet;Bernard Mazoyer

  • Binary-image comparison with local-dissimilarity quantification

    ítienne Baudrier;Frédéric Nicolier;Gilles Millon;Su Ruan

  • TUMOR SEGMENTATION FROM A MULTISPECTRAL MRI IMAGES BY USING SUPPORT VECTOR MACHINE CLASSIFICATION

    Su Ruan;S. Lebonvallet;A. Merabet;J.-M. Constans

  • Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields

    R. Trullo;C. Petitjean;S. Ruan;B. Dubray

  • Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism.

    Tongxue Zhou;Tongxue Zhou;Tongxue Zhou;Stéphane Canu;Stéphane Canu;Su Ruan;Su Ruan

  • Medical Image Synthesis with Context-Aware Generative Adversarial Networks

    Dong Nie;Roger Trullo;Caroline Petitjean;Su Ruan

Frequent Co-Authors

Stéphane Canu
Stéphane Canu Institut National des Sciences Appliquées de Rouen
Jalal M. Fadili
Jalal M. Fadili École Nationale Supérieure d'Ingénieurs de Caen
Bernard Mazoyer
Bernard Mazoyer University of Bordeaux
Thierry Denoeux
Thierry Denoeux University of Technology of Compiègne
Mark A. Anastasio
Mark A. Anastasio University of Illinois at Urbana-Champaign
Jing-Hao Xue
Jing-Hao Xue University College London
Dinggang Shen
Dinggang Shen ShanghaiTech University
Dong Nie
Dong Nie University of North Carolina at Chapel Hill
Liming Chen
Liming Chen École Centrale de Lyon
Arman Rahmim
Arman Rahmim University of British Columbia

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

If you’re considering a future in Computer Science but need flexible options, there are many online pathways to explore. For those just starting out or looking for an affordable route, pursuing 2 year online degrees can be a smart choice. These programs offer a solid technical foundation and can easily lead to entry-level tech opportunities or transfer options for a bachelor’s degree.

Affordability is also a common concern. Fortunately, there are plenty of cheapest online college programs available, allowing you to learn valuable skills without accumulating excessive debt. Low tuition doesn’t mean low quality, with many reputable schools offering standout computer science courses.

If you’re thinking about advancing your qualifications, consider graduate degrees that are worth it in computer science or related fields. These degrees can open doors to specialized and high-demand careers in tech. For applicants worried about GPA requirements, there are also online graduate schools with low gpa requirements that remove hurdles and make advanced study more accessible.

Best Scientists Citing Su Ruan

Trending Scientists

Recently Published Articles