Pr. Su Ruan

Pr. Su Ruan

Fonction : Professeur des Universités

Groupe : Quantif

Biographie

Su RUAN
Full Professor at the University of Rouen Normandy, France

Education and Diplomas
• Jan. 1993 Ph.D. , University of Rennes I, France, Jan. 1993
(Thèse de doctorat, Laboratoire de Traitement du Signal et de l’Image (INSERM-LTSI), soutenue à l’Université de Rennes I)
• Dec. 2000 Professorship Diploma HDR, University of Caen, Dec. 2000.
(Habilitation à Diriger des Recherches (HDR) , Labo. GREYC UMR 6072 CNRS, soutenue à l’Université de Caen.)

Professional Activities
• 1992 – 1993 Assistant professor at the National Institute for Applied Sciences of Rennes, France
(ATER à l’INSA de Rennes)
• 1993 – 2003 Associate professor at the University of Caen, France
(Maître de conférences à l’Université de Caen)
• 2003 – 2010 Full professor at the University of Reims Champagne-Ardenne, France
(Professeure des universités à l’IUT de Troyes de l’Université de Reims Champagne-Ardenne)
• Since 2010 Full professor at the University of Rouen Normandy, France
(Professeure des universités à l’Université de Rouen Normandie)
• 2010-2024 Co-leader of the team QUANTIF
(Co-responsable de l’équipe Quantif)
• 2014-2021 Co-Animator of the « Analysis and image processing » group of Normastic research federation (CNRS FR n°3638)
(Co-Animatrice de l’axe « Analyse et traitement d’images » de la fédération de recherche Normastic, CNRS FR n°3638)
• 2015-2023 Deputy Scientific Director of GDR-ISIS (Directeur scientifique adjoint du GDR-ISIS)
• 2019-2025 Member of National Council of Universities (Membre de la section CNU 61) : https://www.conseil-national-des-universites.fr/cnu/#/ 
• Since 2025 Member of the National Committee for Scientific Research ( Membre du Comité national de la recherche scientifique) : https://www.cnrs.fr/comitenational/sections/section_acc.htm
• Since 2023 Board member of GRETSI (Groupe de Recherche et d’Etudes de Traitement du Signal et des Images) (Membre CA du GRETSI) : https://www.gretsi.fr/membres
• Since 2017 Board member of SFGBM (Société Française de Génie Biologique et Médical) (Membre CA de la SFGMB ) : https://sfgbm.fr/
https://sfgbm.fr/archives/10907#more-10907
https://sfgbm.fr/archives/11202
https://www.sfgbm.fr/archives/11353

• Associate Editor or Area Editor for 3 Elsevier journals
Computerized Medical Imaging and Graphic:
https://www.sciencedirect.com/journal/computerized-medical-imaging-and-graphics
Array : https://www.sciencedirect.com/journal/array
IRBM : https://www.journals.elsevier.com/irbm
• HECKTOR challenge at MICCAI 2022
https://hecktor.grand-challenge.org/Overview

Research Areas
(Thèmes de recherche développés)
Machine learning & Deep learning, theory of belief functions for
a) Image segmentation et classification
b) Information fusion
c) Outcome prediction
d) Applications in medical imaging (IRM; PET/CT)

Teaching (Enseignement) :
Master Ingénierie de la Santé, Ingénierie pour le Bio-médical- IBIOM (Master of Health Engineering: Bio-medical Engineering)
• Fondation and Manager of the IBIOM M1 and M2 master’s program (Porteur et Responsable du parcours master IBIOM M1 et M2)
https://formation.univ-rouen.fr/fr/catalogue-de-l-offre-de-formation/master-lmd-XB/master-ingenierie-de-la-sante-L5CKUPEO/master-ingenierie-de-la-sante-ingenierie-pour-le-bio-medical-L5CKUQEX.html

Publications récentes :
List of publications: https://scholar.google.fr/citations?user=mjB2a6MAAAAJ&hl=fr

1.Tongxue Zhou, Ph.D. Su Ruan Baiying Lei, “BUFNet: Boundary-aware and Uncertainty-driven Multi-modal Fusion Network for MR Brain Tumor Segmentation”, Medical Image Analysis, Volume 107, Part B, 103855, January 2026.

2.Ling Huang, Yucheng Xing, Qika Lin, Jinming Duan, Su Ruan, Mengling Feng, “EsurvFusion: An evidential multimodal survival fusion model based on Epistemic random fuzzy sets”, IEEE Transactions on Fuzzy Systems, doi: 10.1109/TFUZZ.2025.3623879, 2026.

3.Xiaoyan Kui, Zexin Ji, Beiji Zou, Yang Li, Yulan Dai, Liming Chen, Pierre Vera, Su Ruan, “Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution”, IEEE Transactions on Computational Imaging, vol. 11, pp. 827-838, 2025, doi: 10.1109/TCI.2025.3577340.

4.Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan, “Multi-modal MRI synthesis with conditional latent diffusion models for data augmentation in tumor segmentation”, Elsevier, Computerized Medical Imaging and Graphics, Volume 123, 102532, 2025.

5.Ling Huang, Su Ruan, Pierre Decazes, Thierry Denœux, “Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation”, Elsevier, Information Fusion. Volume 113, 102648, January 2025. DOI: https://doi.org/10.1016/j.inffus.2024.102648

6.Zexin Ji, Beiji Zou, Xiaoyan Kui, Hua Li, Pierre Vera, Su Ruan “Generation of Super-Resolution for Medical Image via a Self-prior Guided Mamba Network with Edge-aware Constraint”, Elsevier Pattern Recognition letters, Volume 187, Pages 93-99, January 2025. https://doi.org/10.1016/j.patrec.2024.11.020.

7.Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan, “Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes”, Elsevier, Neurocomputing, Volume 606, 14 November 2024, 128360. DOI: https://doi.org/10.1016/j.neucom.2024.128360. arXiv preprint arXiv:2406.11659.

8.Ling Huang, Su Ruan, Yucheng Xing, Mengling Feng, “A review of uncertainty quantification in medical image analysis: probabilistic and nonprobabilistic methods”, Elsevier Medical Image Analysis, Volume 97, October 2024, 103223. DOI: 10.1016/j.media.2024.103223

9.Zong Fan, Xiaohui Zhang, Su Ruan, Wade Thorstad, Hiram Gay, Pengfei Song, Xiaowei Wang, Hua Li, “A medical image classification method based on self-regularized adversarial learning”, Medical Physics, July 2024. DOI: https://doi.org/10.1002/mp.17320

10.F. Ghazouani, P. Vera, S. Ruan, « Efficient brain tumor segmentation using Swin transformer and enhanced local self-attention», Springer International Journal of Computer Assisted Radiology and Surgery, Volume 19, pages 273–281, 2024. https://doi.org/10.1007/s11548-023-03024-8

Publications



231 documents

  • Zhengshan Huang, Yu Guo, Ning Zhang, Xian Huang, Pierre Decazes, et al.. Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images. Computers in Biology and Medicine, 2022, 151 (Pt A), pp.106230. ⟨10.1016/j.compbiomed.2022.106230⟩. ⟨hal-03842207⟩
  • Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan. Multi-task multi-scale learning for outcome prediction in 3D PET images. Computers in Biology and Medicine, 2022, 151 (Part A), pp.106208. ⟨10.1016/j.compbiomed.2022.106208⟩. ⟨hal-03842217⟩
  • Jannane Nada, Sébastien Bougleux, Jérôme Lapuyade-Lahorgue, Su Ruan, Fethi Ghazouani. MR image synthesis using Riemannian geometry constrained in VAE. 16th IEEE International Conference on Signal Processing (ICSP), IEEE Beijing Section; Beijing Jiaotong University, Oct 2022, Beijing, China. pp.485-488, ⟨10.1109/ICSP56322.2022.9965357⟩. ⟨hal-03842257⟩
  • Abdelouahad Achmamad, Fethi Ghazouani, Su Ruan. Few shot learning for brain tumor segmentation. 16th IEEE ICSP, Oct 2022, Chine, China. ⟨10.1109/ICSP56322.2022.9965315⟩. ⟨hal-03842278⟩
  • Vincent Andrearczyk, Valentin Oreiller, Moamen Abobakr, Azadeh Akhavanallaf, Panagiotis Balermpas, et al.. Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. Head and Neck Tumor Segmentation and Outcome Prediction - Third Challenge, Sep 2022, Singapore, Singapore. pp.1-30, ⟨10.1007/978-3-031-27420-6_1⟩. ⟨hal-04117234⟩
  • Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan. Evidence fusion with contextual discounting for multi-modality medical image segmentation. 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), Sep 2022, Singapour, Singapore. pp.401-411, ⟨10.1007/978-3-031-16443-9_39⟩. ⟨hal-03835981⟩
  • Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan. Apprentissage profond multitâche pour la prédiction de la récidive du cancer utilisant l’entropie d’Havrda-Charvat. Apprentissage profond multitâche pour la prédiction de la récidive du cancer utilisant l’entropie d’Havrda-Charvat, Sep 2022, Nancy, France. ⟨hal-03710358⟩
  • Tongxue Zhou, Alexandra Noeuveglise, Fethi Ghazouani, Romain Modzelewski, Sébastien Thureau, et al.. Prediction of brain tumor recurrence location based on Kullback–Leibler divergence and nonlinear correlation learning. 26th International Conference on Pattern Recognition (ICPR), Aug 2022, Montréal, Canada. pp.4414-4419, ⟨10.1109/ICPR56361.2022.9956094⟩. ⟨hal-03710336⟩
  • Tongxue Zhou, Pierre Vera, Stéphane Canu, Su Ruan. Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation. Pattern Recognition Letters, 2022, 158, pp.125-132. ⟨10.1016/j.patrec.2022.04.019⟩. ⟨hal-03710281⟩
  • Fethi Ghazouani, Tongxue Zhou, Alexandra Noeuveglise, Romain Modzelewski, Sébastien Thureau, et al.. Prédiction de la localisation de la récidive de la tumeur cérébrale basée sur la fusion via l'apprentissage profond. RITS 2022 : Recherche en Imagerie et Technologies pour la Santé, SFGBM (Société Française du Génie Biologique et Médical); LaTIM (INSERM, UMR 1101), May 2022, Brest, France. ⟨hal-03710367⟩