Xiran Jiang

1.4k total citations
61 papers, 986 citations indexed

About

Xiran Jiang is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Xiran Jiang has authored 61 papers receiving a total of 986 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Radiology, Nuclear Medicine and Imaging, 22 papers in Pulmonary and Respiratory Medicine and 16 papers in Biomedical Engineering. Recurrent topics in Xiran Jiang's work include Radiomics and Machine Learning in Medical Imaging (36 papers), Medical Imaging Techniques and Applications (12 papers) and Lung Cancer Diagnosis and Treatment (10 papers). Xiran Jiang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (36 papers), Medical Imaging Techniques and Applications (12 papers) and Lung Cancer Diagnosis and Treatment (10 papers). Xiran Jiang collaborates with scholars based in China, United Kingdom and United States. Xiran Jiang's co-authors include Yahong Luo, Yue Dong, Guodong Sui, Wenwen Jing, Sixiu Liu, Xiaoyu Wang, Tao Yu, Huazhe Yang, Tao Yu and Hairong Zheng and has published in prestigious journals such as SHILAP Revista de lepidopterología, Analytical Chemistry and IEEE Access.

In The Last Decade

Xiran Jiang

58 papers receiving 971 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiran Jiang China 19 558 373 237 128 114 61 986
Xiaoning Wang China 15 446 0.8× 80 0.2× 290 1.2× 47 0.4× 67 0.6× 44 711
Yanling Wen China 11 402 0.7× 345 0.9× 95 0.4× 66 0.5× 30 0.3× 21 701
Anna Carla Bozzini Italy 16 492 0.9× 299 0.8× 253 1.1× 226 1.8× 48 0.4× 61 1.0k
Yongyan Gao China 13 295 0.5× 193 0.5× 238 1.0× 77 0.6× 112 1.0× 18 795
John Kang United States 15 142 0.3× 77 0.2× 153 0.6× 46 0.4× 266 2.3× 40 861
Beijian Huang China 23 369 0.7× 333 0.9× 491 2.1× 69 0.5× 263 2.3× 72 1.3k
Aileen O’Shea United States 13 240 0.4× 70 0.2× 173 0.7× 41 0.3× 72 0.6× 46 545
Si‐Min Ruan China 14 351 0.6× 87 0.2× 91 0.4× 68 0.5× 18 0.2× 43 657
Yoshifumi Noda Japan 22 968 1.7× 506 1.4× 217 0.9× 14 0.1× 27 0.2× 137 1.4k

Countries citing papers authored by Xiran Jiang

Since Specialization
Citations

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

Fields of papers citing papers by Xiran Jiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiran Jiang

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

All Works

20 of 20 papers shown
1.
2.
Wang, Lu, Nan‐Jie Xu, Xiran Jiang, et al.. (2023). A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images. iScience. 26(7). 107005–107005. 7 indexed citations
3.
Liu, Shijie, et al.. (2023). Effective use of prior information for high-performance embryo grading. Biomedical Signal Processing and Control. 85. 104943–104943. 2 indexed citations
4.
Fan, Ying, et al.. (2023). Multiregional radiomics of brain metastasis can predict response to EGFR-TKI in metastatic NSCLC. European Radiology. 33(11). 7902–7912. 12 indexed citations
5.
Fan, Ying, Huanhuan Chen, Xiaoyu Wang, et al.. (2023). Brain‐Tumor Interface‐Based MRI Radiomics Models to Determine EGFR Mutation, Response to EGFR‐TKI and T790M Resistance Mutation in Non‐Small Cell Lung Carcinoma Brain Metastasis. Journal of Magnetic Resonance Imaging. 58(6). 1838–1847. 10 indexed citations
6.
Jiang, Tao, Huazhe Yang, Yue Dong, et al.. (2022). Multiparametric MRI-based radiomics for the prediction of microvascular invasion in hepatocellular carcinoma. Acta Radiologica. 64(2). 456–466. 10 indexed citations
7.
8.
Shi, Jiaxin, Zilong Zhao, Tao Jiang, et al.. (2022). A deep learning approach with subregion partition in MRI image analysis for metastatic brain tumor. Frontiers in Neuroinformatics. 16. 973698–973698. 5 indexed citations
9.
Fan, Ying, Yue Dong, Hongbo Wang, et al.. (2022). Development and externally validate MRI-based nomogram to assess EGFR and T790M mutations in patients with metastatic lung adenocarcinoma. European Radiology. 32(10). 6739–6751. 15 indexed citations
10.
Wang, Xiaoyu, Guanyu Liu, Wenwen Jing, et al.. (2022). Multi-parametric MRI-based radiomics for the diagnosis of malignant soft-tissue tumor. Magnetic Resonance Imaging. 91. 91–99. 10 indexed citations
11.
Yu, Tao, et al.. (2022). Multi-parametric MRI-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer. SHILAP Revista de lepidopterología. 28(4). 312–321. 22 indexed citations
12.
Liu, Jiao, Jiangdian Song, Yu Zhao, et al.. (2022). LWMA-Net: Light-weighted morphology attention learning for human embryo grading. Computers in Biology and Medicine. 151(Pt B). 106242–106242. 5 indexed citations
13.
Jiang, Xiran, et al.. (2021). MultiparametricMRI‐Based Radiomics Approaches for Preoperative Prediction ofEGFRMutation Status in Spinal Bone Metastases in Patients with Lung Adenocarcinoma. Journal of Magnetic Resonance Imaging. 54(2). 497–507. 30 indexed citations
14.
15.
Shi, Jiaxin, Yue Dong, Wenyan Jiang, et al.. (2021). MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study. Magnetic Resonance Imaging. 88. 1–8. 38 indexed citations
16.
Jiang, Tao, Jiangdian Song, Xiaoyu Wang, et al.. (2021). Intratumoral and Peritumoral Analysis of Mammography, Tomosynthesis, and Multiparametric MRI for Predicting Ki-67 Level in Breast Cancer: a Radiomics-Based Study. Molecular Imaging and Biology. 24(4). 550–559. 30 indexed citations
17.
Jiang, Xiran, Yangyang Kan, Tao Yu, et al.. (2020). MRI Based Radiomics Approach With Deep Learning for Prediction of Vessel Invasion in Early-Stage Cervical Cancer. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18(3). 995–1002. 61 indexed citations
18.
Zhang, Siqi, Lei Wang, Jie Zhang, et al.. (2020). Consecutive Context Perceive Generative Adversarial Networks for Serial Sections Inpainting. IEEE Access. 8. 190417–190430. 4 indexed citations
19.
Zheng, Lulu, Yongfeng Fu, Xiran Jiang, et al.. (2015). Microfluidic system for high-throughput immunoglobulin-E analysis from clinical serum samples. Talanta. 143. 83–89. 8 indexed citations
20.
Jiang, Xiran, Ning Shao, Wenwen Jing, et al.. (2014). Microfluidic chip integrating high throughput continuous-flow PCR and DNA hybridization for bacteria analysis. Talanta. 122. 246–250. 59 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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