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)
• Since 2019 Member of National Council of Universities (Membre de la section CNU 61)
https://www.conseil-national-des-universites.fr/cnu/#/
• 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)
List of publications: https://scholar.google.fr/citations?user=mjB2a6MAAAAJ&hl=fr
Publications associées
- A literature survey of MR-based brain tumor segmentation with missing Modalities
Comput Med Imaging Graph · 2023 - Application of belief functions to medical image segmentation: A review.
Information Fusion · 2023 - Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning.
Comput Med Imaging Graph. · 2023 - Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review.
J Imaging. · 2023 - Semi-Supervised Multiple Evidence Fusion for Brain Tumor Segmentation.
Neurocomputing · 2023 - Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review
MDPI Journal of Imaging · 2023 - Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images.
Comput Biol Med · 2022 - Multi-task multi-scale learning for outcome prediction in 3D PET images.
Comput Biol Med · 2022 - A Tri-attention Fusion Guided Multi-modal Segmentation Network Pattern Recognition
Elsevier, Pattern Recognition · 2022 - Deep Co-supervision and Attention Fusion Strategy for Automatic COVID-19 Lung Infection Segmentation on CT Images
Pattern Recognition · 2022 - Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation
Pattern Recognition Letters · 2022 - Lymphoma segmentation from 3D PET-CT images using a deep evidential network.
International Journal of Approximate Reasoning · 2022 - Segmentation of multicorrelated images with copula models and conditionally random fields.
J Med Imaging (Bellingham) · 2022 - AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging:Towards Radiophenomics.
PET Clin · 2022 - Learning-based Cancer Treatment Outcome Prognosis using Multimodal Biomarkers.
IEEE Trans Radiat Plasma Med Sci · 2022 - Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction.
J Imaging · 2022 - Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities.
IEEE Trans Image Process · 2021 - A novel machine learning approach for cancer treatment prognosis and its applications in oropharyngeal cancer with microRNA biomarkers.
Oxford Academic, Bioinformatics · 2021 - Deep Learning Using Havrda-Charvat Entropy for Classification of Pulmonary Optical Endomicroscopy.
IRBM · 2021 - Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities.
Neurocomputing · 2021 - Advanced Computational Intelligence in Medical and Biomedical Imaging.
IRBM · 2021 - Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism.
Int J Imaging Syst Technol · 2020 - Fusion based on attention mechanism and context constraint for multi-modal brain tumor segmentation.
Comput Med Imaging Graph · 2020 - Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation.
Comput Biol Med · 2020 - Incoherent dictionary learning via mixed-integer programming and hybrid augmented Lagrangian.
Digital Signal Processing · 2020 - Joint tumor segmentation in PET-CT images using co-clustering and fusion based on belief functions.
IEEE Trans Image Process · 2019 - Mixed integer programming for sparse coding : application to image denoising.
IEEE Trans Comput Imaging · 2019 - Detection and segmentation of lymphomas in 3D PET images via clustering with entropy-based optimization strategy.
Int J Comput Assist Radiol Surg. · 2019 - A review : Deep learning for medical image segmentation using multi-modality fussion.
Elsevier, Array, · 2019 - Adaptive kernelized evidential clustering for automatic 3D tumor segmentation in FDG-PET images.
Multimedia Systems · 2019 - Multiorgan segmentation using distance-aware adversarial networks.
SPIE Journal of Medical Imaging · 2019 - 3D RPET-NET: Development of a 3D PET Imaging Convolutional Neural Network for Radiomics Analysis and Outcome Prediction.
2019
2019