Yun Jiang

1.8k total citations · 1 hit paper
64 papers, 1.3k citations indexed

About

Yun Jiang is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Ophthalmology. According to data from OpenAlex, Yun Jiang has authored 64 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Radiology, Nuclear Medicine and Imaging, 21 papers in Computer Vision and Pattern Recognition and 15 papers in Ophthalmology. Recurrent topics in Yun Jiang's work include Retinal Imaging and Analysis (17 papers), Glaucoma and retinal disorders (11 papers) and Digital Imaging for Blood Diseases (10 papers). Yun Jiang is often cited by papers focused on Retinal Imaging and Analysis (17 papers), Glaucoma and retinal disorders (11 papers) and Digital Imaging for Blood Diseases (10 papers). Yun Jiang collaborates with scholars based in China, United States and Russia. Yun Jiang's co-authors include Li Chen, Xiao Xiao, Hai Zhang, Jing Liang, Jeffrey Tsao, Farong Huang, Lei Du, Г. В. Ланский, Hongzhi Zhang and Zhi‐Hui Kang and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of Colloid and Interface Science.

In The Last Decade

Yun Jiang

61 papers receiving 1.2k citations

Hit 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.

SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer

2022 150 citations Yun Jiang, Jing Liang et al. Brain Sciences profile →

Peers

Yun Jiang

RNMI

CVPR

AI

OPHTH

MC

Praveer Singh United States

Yong Huang China

Zhihao Wu China

Ye Luo China

Xiaoxia Yin Australia

Masahiro Oda Japan

Liangqiong Qu China

Dahong Qian China

Giorgos Papanastasiou United Kingdom

Hongrun Zhang China

Praveer Singh United States

Yun Jiang

519 ×0.9

RNMI

253 ×0.5

CVPR

259 ×0.8

AI

76 ×0.4

OPHTH

60 ×0.3

MC

Citations per year, relative to Yun Jiang Yun Jiang (= 1×) peers Praveer Singh

Countries citing papers authored by Yun Jiang

Since Specialization

Citations

This map shows the geographic impact of Yun Jiang'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 Yun Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yun Jiang more than expected).

Fields of papers citing papers by Yun Jiang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yun Jiang. 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 Yun Jiang. The network helps show where Yun Jiang may publish in the future.

Co-authorship network of co-authors of Yun Jiang

This figure shows the co-authorship network connecting the top 25 collaborators of Yun Jiang. A scholar is included among the top collaborators of Yun Jiang 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 Yun Jiang. Yun Jiang 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

1.

Jiang, Yun, et al.. (2025). Exploring Contrastive Pre-Training for Domain Connections in Medical Image Segmentation. IEEE Transactions on Medical Imaging. 44(4). 1686–1698. 1 indexed citations

2.

Ai, Bin, Yun Jiang, Zengyao Wang, et al.. (2024). Swiss roll nanoarrays for chiral plasmonic photocatalysis. Journal of Colloid and Interface Science. 678(Pt A). 818–826. 2 indexed citations

3.

Jiang, Yun, et al.. (2024). Dense-sparse representation matters: A point-based method for volumetric medical image segmentation. Journal of Visual Communication and Image Representation. 100. 104115–104115. 1 indexed citations

4.

Liang, Jing, et al.. (2024). Skip connection information enhancement network for retinal vessel segmentation. Medical & Biological Engineering & Computing. 62(10). 3163–3178. 1 indexed citations

5.

Liu, Yuchi, et al.. (2023). Simultaneous mapping of T1, T2, and T2* at 0.55T with Rosette MR Fingerprinting. Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition. 1 indexed citations

6.

Yao, Baodong, et al.. (2023). Association of serum folate with prevalence of non-alcoholic fatty liver disease among adults (NHANES 2011–2018). Frontiers in Nutrition. 10. 1141156–1141156. 12 indexed citations

7.

Jiang, Yun, et al.. (2023). MS-CANet: Multi-Scale Subtraction Network with Coordinate Attention for Retinal Vessel Segmentation. Symmetry. 15(4). 835–835.

8.

Jiang, Yun, et al.. (2023). PCF-Net: Position and context information fusion attention convolutional neural network for skin lesion segmentation. Heliyon. 9(3). e13942–e13942. 12 indexed citations

9.

Jiang, Yun, et al.. (2022). MCPANet: Multiscale Cross-Position Attention Network for Retinal Vessel Image Segmentation. Symmetry. 14(7). 1357–1357. 5 indexed citations

10.

Jiang, Yun, et al.. (2022). SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer. Brain Sciences. 12(6). 797–797. 150 indexed citations breakdown →

11.

Jiang, Yun, et al.. (2022). MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN. Sensors. 22(12). 4592–4592. 18 indexed citations

12.

Jiang, Yun, et al.. (2022). SIL-Net: A Semi-Isotropic L-shaped network for dermoscopic image segmentation. Computers in Biology and Medicine. 150. 106146–106146. 6 indexed citations

13.

Jiang, Yun, et al.. (2022). Dermoscopic image segmentation based on Pyramid Residual Attention Module. PLoS ONE. 17(9). e0267380–e0267380. 4 indexed citations

14.

Jiang, Yun, et al.. (2021). Attention-Guided Network with Densely Connected Convolution for Skin Lesion Segmentation. Sensors. 21(10). 3462–3462. 17 indexed citations

15.

Jiang, Yun, et al.. (2020). Distance image segmentation method for terminal sensitive missile linear array laser radar. 49(1).

16.

Jiang, Yun, et al.. (2020). Joint disc and cup segmentation based on recurrent fully convolutional network. PLoS ONE. 15(9). e0238983–e0238983. 18 indexed citations

17.

Jiang, Yun, Li Chen, Hai Zhang, & Xiao Xiao. (2019). Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PLoS ONE. 14(3). e0214587–e0214587. 238 indexed citations

18.

Tsao, Jeffrey & Yun Jiang. (2012). Hierarchical IDEAL: Fast, robust, and multiresolution separation of multiple chemical species from multiple echo times. Magnetic Resonance in Medicine. 70(1). 155–159. 63 indexed citations

19.

Zhang, Hongzhi, Zhi‐Hui Kang, Yun Jiang, et al.. (2008). SHG phase matching in GaSe and mixed GaSe1_1-xS_x, x0.412, crystals at room temperature. Optics Express. 16(13). 9951–9951. 40 indexed citations

20.

Kang, Zhi‐Hui, Yun Jiang, Hongzhi Zhang, et al.. (2008). SHG in doped GaSe:In crystals. Optics Express. 16(13). 9978–9978. 92 indexed citations

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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