Su Ruan

8.2k total citations · 5 hit papers
171 papers, 5.0k citations indexed

About

Su Ruan is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Su Ruan has authored 171 papers receiving a total of 5.0k indexed citations (citations by other indexed papers that have themselves been cited), including 108 papers in Computer Vision and Pattern Recognition, 76 papers in Radiology, Nuclear Medicine and Imaging and 29 papers in Artificial Intelligence. Recurrent topics in Su Ruan's work include Medical Image Segmentation Techniques (77 papers), Radiomics and Machine Learning in Medical Imaging (51 papers) and Medical Imaging Techniques and Applications (44 papers). Su Ruan is often cited by papers focused on Medical Image Segmentation Techniques (77 papers), Radiomics and Machine Learning in Medical Imaging (51 papers) and Medical Imaging Techniques and Applications (44 papers). Su Ruan collaborates with scholars based in France, China and United States. Su Ruan's co-authors include Tongxue Zhou, Stéphane Canu, Caroline Petitjean, Pierre Véra, Romain Modzelewski, Roger Trullo, Dinggang Shen, Dong Nie, Amine Amyar and D. Bloyet and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Su Ruan

157 papers receiving 4.8k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Medical Image Synthesis with Context-Aware Generative Adversarial Networks

2017 426 citations Su Ruan et al. profile →
Medical Image Synthesis with Deep Convolutional Adversarial Networks

2018 421 citations Su Ruan et al. profile →
A review: Deep learning for medical image segmentation using multi-modality fusion

2019 417 citations Tongxue Zhou, Su Ruan et al. profile →
Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation

2020 411 citations Amine Amyar, Romain Modzelewski et al. Computers in Biology and Medicine profile →
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review

2023 116 citations Jérôme Lapuyade‐Lahorgue, Su Ruan et al. Journal of Imaging profile →

Peers

Countries citing papers authored by Su Ruan

Since Specialization

Citations

This map shows the geographic impact of Su Ruan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Su Ruan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Su Ruan more than expected).

Fields of papers citing papers by Su Ruan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Su Ruan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Su Ruan. The network helps show where Su Ruan may publish in the future.

Co-authorship network of co-authors of Su Ruan

This figure shows the co-authorship network connecting the top 25 collaborators of Su Ruan. A scholar is included among the top collaborators of Su Ruan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Su Ruan. Su Ruan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	A reliable framework for brain tumor segmentation via multi-modal fusion and uncertainty modeling 2025 ·Information Fusion ·Tongxue Zhou, (unknown), Su Ruan, Tingjin Luo, (unknown), Bingbing Jiang, (unknown), (unknown), Defu Yang, Guang Yang	0
2	Global and local Mamba network for multi-modality medical image super-resolution 2025 ·Pattern Recognition ·(unknown), Beiji Zou, Xiaoyan Kui, S. Thureau, Su Ruan	0
3	Generation of super-resolution for medical image via a self-prior guided Mamba network with edge-aware constraint 2024 ·Pattern Recognition Letters ·(unknown), Beiji Zou, Xiaoyan Kui, Hua Li, Pierre Véra, Su Ruan	2
4	Discriminative Hamiltonian variational autoencoder for accurate tumor segmentation in data-scarce regimes 2024 ·Neurocomputing ·(unknown), Jérôme Lapuyade‐Lahorgue, Pierre Véra, Su Ruan	4
5	Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation 2024 ·Information Fusion ·Ling Huang, Su Ruan, Pierre Decazes, Thierry Denœux	17
6	Semi-supervised multiple evidence fusion for brain tumor segmentation 2023 ·Neurocomputing ·Ling Huang, Su Ruan, Thierry Denœux	14
7	Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review breakdown → 2023 ·Journal of Imaging ·(unknown), Jérôme Lapuyade‐Lahorgue, Su Ruan	116
8	Artificial Intelligence-Based Detection, Classification and Prediction/Prognosis in PET Imaging: Towards Radiophenomics 2021 ·arXiv (Cornell University) ·Fereshteh Yousefirizi, (unknown), Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim	1
9	AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging 2021 ·PET Clinics ·Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim	44
10	An automatic COVID-19 CT segmentation based on U-Net with attention mechanism 2020 ·arXiv (Cornell University) ·Tongxue Zhou, Stéphane Canu, Su Ruan	26
11	Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation breakdown → 2020 ·Computers in Biology and Medicine ·Amine Amyar, Romain Modzelewski, Hua Li, Su Ruan	411
12	Mixed Integer Programming For Sparse Coding: Application to Image Denoising 2019 ·IEEE Transactions on Computational Imaging ·(unknown), Stéphane Canu, Paul Honeiné, Su Ruan	9
13	Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions 2017 ·IEEE Transactions on Image Processing ·Jérôme Lapuyade‐Lahorgue, Jing‐Hao Xue, Su Ruan	24
14	An integrated model-driven method for in-treatment upper airway motion tracking using cine MRI in head and neck radiation therapy 2016 ·Medical Physics ·Hua Li, (unknown), (unknown), Harold Li, Benjamin W. Fischer‐Valuck, (unknown), James Dempsey, Su Ruan, Mark A. Anastasio, Thomas R. Mazur, (unknown), Rojano Kashani, Olga Green, Vivian Rodriguez, Hiram A. Gay, Wade L. Thorstad, Sasa Mutic	13
15	Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction 2016 ·Medical Image Analysis ·Chunfeng Lian, Su Ruan, Thierry Denœux, Fabrice Jardin, Pierre Véra	54
16	Fusion of multi-tracer PET images for dose painting 2014 ·Medical Image Analysis ·Benoît Lelandais, Su Ruan, Thierry Denœux, Pierre Véra, Isabelle Gardin	33
17	FCM and Level Set Based Segmentation Method for Brain MR Images 2008 ·Dianzi xuebao ·Su Ruan	4
18	Statistical Shape Model-based Segmentation of brain MRI Images 2007 ·Conference proceedings ·(unknown), Su Ruan, Jean‐Marc Constans	5
19	Brain tissue classification of magnetic resonance images using partial volume modeling 2000 ·IEEE Transactions on Medical Imaging ·Su Ruan, (unknown), Jing‐Hao Xue, Jalal Fadili, D. Bloyet	109
20	Detection of Brain Activation Signal from Functional Magnetic Resonance Imaging Data 1996 ·Journal of Neuroimaging ·Su Ruan, (unknown), Jean‐Marc Constans, D. Bloyet	4

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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