World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
61
Citations
13855
World Ranking
3098
National Ranking
415

Overview

Jiang Liu is affiliated with the Southern University of Science and Technology in China. Their research primarily spans the fields of Medicine and Engineering, with a significant focus on subfields such as Radiology, Nuclear Medicine and Imaging, Ophthalmology, Computer Vision and Pattern Recognition, Biomedical Engineering, and Artificial Intelligence.

Their work covers various topics relating to medical imaging and analysis, particularly in retinal imaging and disorders. The main topics of their research include:

  • Retinal Imaging and Analysis
  • Glaucoma and retinal disorders
  • Retinal Diseases and Treatments
  • Optical Coherence Tomography Applications
  • Retinal and Optic Conditions
  • Advanced Neural Network Applications
  • Medical Image Segmentation Techniques

Jiang Liu has contributed to the scholarly community with numerous publications in leading venues. The most frequent publication venues are:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • SSRN Electronic Journal
  • Research Square (Research Square)
  • Medical Image Analysis

Their recent significant papers highlight contributions in cancer metastasis, cell death pathways, and medical imaging using deep learning. These include:

  • DNA of neutrophil extracellular traps promotes cancer metastasis via CCDC25, 2020, Nature
  • Targeting cell death pathways for cancer therapy: recent developments in necroptosis, pyroptosis, ferroptosis, and cuproptosis research, 2022, Journal of Hematology & Oncology
  • CS 2 -Net: Deep learning segmentation of curvilinear structures in medical imaging, 2020, Medical Image Analysis
  • ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model, 2020, IEEE Transactions on Medical Imaging
  • A map of transcriptional heterogeneity and regulatory variation in human microglia, 2021, Nature Genetics

Jiang Liu frequently collaborates with various researchers, including:

  • Huazhu Fu
  • Yitian Zhao
  • Yan Hu
  • Risa Higashita
  • Xiaoqing Zhang

Overall, their work integrates advanced neural networks and medical imaging technologies, emphasizing retinal health and associated diseases. Their multidisciplinary approach encompasses both clinical and computational aspects within biomedicine and engineering domains.

Best Publications

  • CE-Net: Context Encoder Network for 2D Medical Image Segmentation

    Zaiwang Gu;Jun Cheng;Huazhu Fu;Kang Zhou

  • Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation

    Huazhu Fu;Jun Cheng;Yanwu Xu;Damon Wing Kee Wong

  • Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening

    Jun Cheng;Jiang Liu;Yanwu Xu;Fengshou Yin

  • DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field

    Huazhu Fu;Yanwu Xu;Stephen Lin;Damon Wing Kee Wong

  • Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image

    Huazhu Fu;Jun Cheng;Yanwu Xu;Changqing Zhang

  • Glaucoma detection based on deep convolutional neural network

    Xiangyu Chen;Yanwu Xu;Damon Wing Kee Wong;Tien Yin Wong

  • ORIGA -light : An online retinal fundus image database for glaucoma analysis and research

    Zhuo Zhang;Feng Shou Yin;Jiang Liu;Wing Kee Wong

  • CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging.

    Lei Mou;Yitian Zhao;Huazhu Fu;Yonghuai Liu

  • Retinal vessel segmentation via deep learning network and fully-connected conditional random fields

    Huazhu Fu;Yanwu Xu;Damon Wing Kee Wong;Jiang Liu

  • ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

    Yuhui Ma;Huaying Hao;Jianyang Xie;Huazhu Fu

  • CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation

    Lei Mou;Yitian Zhao;Li Chen;Jun Cheng

  • Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI

    D. W. K. Wong;J. Liu;J.H. Lim;X. Jia

  • Structure and Illumination Constrained GAN for Medical Image Enhancement.

    Yuhui Ma;Jiang Liu;Yonghuai Liu;Huazhu Fu

  • Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces

    Huazhu Fu;Boyang Wang;Jianbing Shen;Shanshan Cui

  • Automatic Feature Learning for Glaucoma Detection Based on Deep Learning

    Xiangyu Chen;Yanwu Xu;Shuicheng Yan;Damon Wing Kee Wong

  • Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis

    Fengshou Yin;Jiang Liu;Damon Wing Kee Wong;Ngan Meng Tan

  • Dense Dilated Network With Probability Regularized Walk for Vessel Detection

    Lei Mou;Li Chen;Jun Cheng;Zaiwang Gu

  • A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images

    Huazhu Fu;Huazhu Fu;Mani Baskaran;Mani Baskaran;Yanwu Xu;Yanwu Xu;Stephen Lin

  • Conceptualizing perceived affordances in social media interaction design

    Yuxiang Zhao;Jiang Liu;Jian Tang;Qinghua Zhu

  • Model-based optic nerve head segmentation on retinal fundus images

    Fengshou Yin;Jiang Liu;Sim Heng Ong;Ying Sun

  • PolyFormer: Referring Image Segmentation as Sequential Polygon Generation

    Unknown

  • Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

    Yitian Zhao;Yalin Zheng;Yonghuai Liu;Yifan Zhao

  • Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images

    Kang Zhou;Yuting Xiao;Jianlong Yang;Jun Cheng

  • Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces

    Huazhu Fu;Boyang Wang;Jianbing Shen;Shanshan Cui

  • CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging

    Lei Mou;Yitian Zhao;Huazhu Fu;Yonghuai Liu

  • ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

    Yuhui Ma;Huaying Hao;Huazhu Fu;Jiong Zhang

  • Medical image analysis

    Baba C. Vemuri;James S. Duncan

Frequent Co-Authors

Damon Wing Kee Wong
Damon Wing Kee Wong Nanyang Technological University
Jun Cheng
Jun Cheng University of Chinese Academy of Sciences
Yanwu Xu
Yanwu Xu South China University of Technology
Yitian Zhao
Yitian Zhao Chinese Academy of Sciences
Huazhu Fu
Huazhu Fu Agency for Science, Technology and Research
Joo-Hwee Lim
Joo-Hwee Lim Agency for Science, Technology and Research
Shenghua Gao
Shenghua Gao ShanghaiTech University
Shijian Lu
Shijian Lu Nanyang Technological University
Lixin Duan
Lixin Duan University of Electronic Science and Technology of China
Chee-Kong Chui
Chee-Kong Chui National University of Singapore

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