Yusong Lin

1.3k total citations
56 papers, 929 citations indexed

About

Yusong Lin is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Neurology. According to data from OpenAlex, Yusong Lin has authored 56 papers receiving a total of 929 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Radiology, Nuclear Medicine and Imaging, 15 papers in Computer Vision and Pattern Recognition and 15 papers in Neurology. Recurrent topics in Yusong Lin's work include Radiomics and Machine Learning in Medical Imaging (20 papers), Brain Tumor Detection and Classification (15 papers) and Medical Image Segmentation Techniques (8 papers). Yusong Lin is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (20 papers), Brain Tumor Detection and Classification (15 papers) and Medical Image Segmentation Techniques (8 papers). Yusong Lin collaborates with scholars based in China, United States and Canada. Yusong Lin's co-authors include Meiyun Wang, Jie Tian, Yaping Wu, Yan Bai, Jinyuan Zhou, Zhenyu Liu, Xiaohua Hong, E. Mark Haacke, Jingliang Cheng and Dapeng Shi and has published in prestigious journals such as PLoS ONE, Scientific Reports and Radiology.

In The Last Decade

Yusong Lin

51 papers receiving 919 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yusong Lin China	18	594	163	156	125	124	56	929
Fabian Isensee Germany	15	578 1.0×	166 1.0×	149 1.0×	87 0.7×	222 1.8×	37	955
Zeju Li China	12	489 0.8×	155 1.0×	232 1.5×	125 1.0×	131 1.1×	24	684
Leonard Sunwoo South Korea	21	695 1.2×	92 0.6×	191 1.2×	303 2.4×	96 0.8×	72	1.2k
Hwan-ho Cho South Korea	15	683 1.1×	163 1.0×	174 1.1×	219 1.8×	54 0.4×	29	819
P. Meyer France	16	394 0.7×	116 0.7×	80 0.5×	229 1.8×	81 0.7×	65	965
Hendrik Laue Germany	13	559 0.9×	124 0.8×	46 0.3×	125 1.0×	99 0.8×	26	884
Luke Macyszyn United States	16	410 0.7×	94 0.6×	274 1.8×	83 0.7×	59 0.5×	44	975
Justin Roper United States	17	643 1.1×	96 0.6×	108 0.7×	319 2.6×	174 1.4×	93	972
Xuxin Chen United States	11	400 0.7×	307 1.9×	42 0.3×	129 1.0×	157 1.3×	40	858
Giles Tetteh Germany	11	462 0.8×	94 0.6×	31 0.2×	189 1.5×	136 1.1×	18	844

Countries citing papers authored by Yusong Lin

Since Specialization

Citations

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

Fields of papers citing papers by Yusong Lin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yusong Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Yusong Lin. A scholar is included among the top collaborators of Yusong Lin 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 Yusong Lin. Yusong Lin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Zhang, Shiqian, et al.. (2025). A collaborative inference strategy for medical image diagnosis in mobile edge computing environment. PeerJ Computer Science. 11. e2708–e2708.

Jiang, Linjun, et al.. (2024). Lightweight deep learning method for end-to-end point cloud registration. Graphical Models. 137. 101252–101252.

Xie, Qian, Yusong Lin, Meiyun Wang, & Yaping Wu. (2024). Synthesis of gadolinium‐enhanced glioma images on multisequence magnetic resonance images using contrastive learning. Medical Physics. 51(7). 4888–4897. 2 indexed citations

Liu, Qi, et al.. (2023). Multi-user multi-objective computation offloading for medical image diagnosis. PeerJ Computer Science. 9. e1239–e1239. 3 indexed citations

Li, Cheng, et al.. (2023). Expert knowledge guided manifold representation learning for magnetic resonance imaging-based glioma grading. Biomedical Signal Processing and Control. 85. 104876–104876. 3 indexed citations

Wang, Meiyun, et al.. (2023). Universal multi-factor feature selection method for radiomics-based brain tumor classification. Computers in Biology and Medicine. 164. 107122–107122. 8 indexed citations

Li, Yamei, et al.. (2022). Short-Axis PET Image Quality Improvement by Attention CycleGAN Using Total-Body PET. Journal of Healthcare Engineering. 2022. 1–13. 4 indexed citations

Lin, Yusong, et al.. (2022). Deep residual-SVD network for brain image registration. Physics in Medicine and Biology. 67(14). 144002–144002. 2 indexed citations

Bai, Jie, Longfei Li, Peipei Wang, et al.. (2021). AI-Powered Radiomics Algorithm Based on Slice Pooling for the Glioma Grading. IEEE Transactions on Industrial Informatics. 18(8). 5383–5393. 5 indexed citations

10.

Wu, Yaping, et al.. (2021). Fproi-GAN with Fused Regional Features for the Synthesis of High-Quality Paired Medical Images. Journal of Healthcare Engineering. 2021. 1–13. 2 indexed citations

11.

Li, Longfei, Ke Wang, Xiujian Ma, et al.. (2019). Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma. European Journal of Radiology. 118. 81–87. 50 indexed citations

12.

Kong, Ziren, Jiatong Li, Zehua Liu, et al.. (2019). Radiomics signature based on FDG-PET predicts proliferative activity in primary glioma. Clinical Radiology. 74(10). 815.e15–815.e23. 20 indexed citations

13.

Kong, Ziren, Yusong Lin, Longfei Li, et al.. (2019). 18F-FDG-PET-based Radiomics signature predicts MGMT promoter methylation status in primary diffuse glioma. Cancer Imaging. 19(1). 58–58. 39 indexed citations

14.

Wu, Qingxia, Kuan Yao, Zhenyu Liu, et al.. (2019). Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study. EBioMedicine. 50. 355–365. 48 indexed citations

15.

Wu, Yaping, et al.. (2019). Automatic glioma segmentation based on adaptive superpixel. BMC Medical Imaging. 19(1). 73–73. 36 indexed citations

16.

Tan, Hongna, Yaping Wu, Jing Zhou, et al.. (2019). Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Carcinoma Using Radiomics Features Based on the Fat-Suppressed T2 Sequence. Academic Radiology. 27(9). 1217–1225. 50 indexed citations

17.

Fan, Yanghua, Zhenyu Liu, Bo Hou, et al.. (2019). Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma. European Journal of Radiology. 121. 108647–108647. 35 indexed citations

18.

Lin, Yusong, et al.. (2018). Automated glioma detection and segmentation using graphical models. PLoS ONE. 13(8). e0200745–e0200745. 17 indexed citations

19.

Lin, Yusong, Yan Bai, Peng Liu, et al.. (2017). Alterations in regional homogeneity of resting-state cerebral activity in patients with chronic prostatitis/chronic pelvic pain syndrome. PLoS ONE. 12(9). e0184896–e0184896. 9 indexed citations

20.

Ma, Xiaoyue, Yan Bai, Yusong Lin, et al.. (2017). Amide proton transfer magnetic resonance imaging in detecting intracranial hemorrhage at different stages: a comparative study with susceptibility weighted imaging. Scientific Reports. 7(1). 45696–45696. 20 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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