Haojiang Li

1.2k total citations
80 papers, 766 citations indexed

About

Haojiang Li is a scholar working on Otorhinolaryngology, Surgery and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Haojiang Li has authored 80 papers receiving a total of 766 indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Otorhinolaryngology, 33 papers in Surgery and 26 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Haojiang Li's work include Head and Neck Cancer Studies (44 papers), Radiomics and Machine Learning in Medical Imaging (26 papers) and Head and Neck Surgical Oncology (20 papers). Haojiang Li is often cited by papers focused on Head and Neck Cancer Studies (44 papers), Radiomics and Machine Learning in Medical Imaging (26 papers) and Head and Neck Surgical Oncology (20 papers). Haojiang Li collaborates with scholars based in China, United States and Macao. Haojiang Li's co-authors include Lizhi Liu, Chunyan Cui, Guangying Ruan, Annan Dong, Lizhi Liu, Jian Zhou, Ming‐Yuan Chen, Jun Shen, Jian Zhou and Shaobo Liang and has published in prestigious journals such as IEEE Access, Advanced Science and Information Sciences.

In The Last Decade

Haojiang Li

69 papers receiving 761 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Haojiang Li China 16 407 317 208 146 125 80 766
Changjuan Tao China 12 321 0.8× 273 0.9× 130 0.6× 188 1.3× 157 1.3× 29 703
Chuan-Miao Xie China 15 309 0.8× 224 0.7× 199 1.0× 202 1.4× 208 1.7× 29 758
Fayu Liu China 14 143 0.4× 159 0.5× 337 1.6× 84 0.6× 110 0.9× 34 671
Shao-Min Huang China 14 295 0.7× 550 1.7× 330 1.6× 307 2.1× 214 1.7× 18 864
Guanzhong Gong China 15 470 1.2× 72 0.2× 65 0.3× 239 1.6× 49 0.4× 91 753
Yanfeng Zhao China 18 408 1.0× 64 0.2× 72 0.3× 231 1.6× 72 0.6× 71 836
X. Sharon Qi United States 21 585 1.4× 89 0.3× 95 0.5× 478 3.3× 118 0.9× 86 1.1k
Jianghong Xiao China 16 362 0.9× 82 0.3× 67 0.3× 145 1.0× 35 0.3× 48 689
Wenbing Lv China 15 874 2.1× 116 0.4× 97 0.5× 424 2.9× 183 1.5× 44 1.0k
Charlotte L. Brouwer Netherlands 21 1.2k 3.0× 582 1.8× 201 1.0× 634 4.3× 99 0.8× 54 1.9k

Countries citing papers authored by Haojiang Li

Since Specialization
Citations

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

Fields of papers citing papers by Haojiang Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Haojiang Li

This figure shows the co-authorship network connecting the top 25 collaborators of Haojiang Li. A scholar is included among the top collaborators of Haojiang Li 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 Haojiang Li. Haojiang Li 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.
Lin, Yi, Wanyuan Chen, Guangying Ruan, et al.. (2025). Risk classification of thymoma based on multi‐feature fusion in dynamic enhanced CT. Medical Physics. 52(7). e17968–e17968.
2.
Li, Haojiang, et al.. (2025). Controversies in hypertension therapy: bedtime dosing or daytime dosing?. Journal of Hypertension. 43(7). 1126–1135.
3.
Kou, Kit Ian, et al.. (2024). L2,1-norm regularized quaternion matrix completion using sparse representation and approximate QSVD. Neurocomputing. 615. 128823–128823.
4.
5.
Chen, Zhi, Yuhua Huang, Wen J. Li, et al.. (2024). Motion and anatomy dual aware lung ventilation imaging by integrating Jacobian map and average CT image using dual path fusion network. Medical Physics. 52(1). 246–256. 3 indexed citations
6.
Wang, Shunxin, Yi Yang, Haojiang Li, et al.. (2024). Radiomics-based nomogram guides adaptive de-intensification in locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy. European Radiology. 34(10). 6831–6842. 4 indexed citations
7.
Tian, Zhenzhou, et al.. (2024). HardVD: High-capacity cross-modal adversarial reprogramming for data-efficient vulnerability detection. Information Sciences. 686. 121370–121370. 3 indexed citations
8.
Zhou, Ta, Yuanpeng Zhang, Shu‐Cheng Chen, et al.. (2024). Non-contrasted computed tomography (NCCT) based chronic thromboembolic pulmonary hypertension (CTEPH) automatic diagnosis using cascaded network with multiple instance learning. Physics in Medicine and Biology. 69(18). 185011–185011. 2 indexed citations
9.
Li, Haojiang, et al.. (2023). BSMM-Net: Multi-modal neural network based on bilateral symmetry for nasopharyngeal carcinoma segmentation. Frontiers in Human Neuroscience. 16. 1068713–1068713. 1 indexed citations
10.
Zhao, Xin, et al.. (2023). MWSR-YLCA: Improved YOLOv7 Embedded with Attention Mechanism for Nasopharyngeal Carcinoma Detection from MR Images. Electronics. 12(6). 1352–1352. 1 indexed citations
11.
Li, Haojiang, et al.. (2023). Deep Active Learning for Computer-Aided Detection of Nasopharyngeal Carcinoma in MRI Images. 306–315. 1 indexed citations
12.
Li, Haojiang, et al.. (2023). Design of Intelligent Robot for Drug Delivery. 300–304.
13.
14.
Li, Haojiang, Di Cao, Shuqi Li, et al.. (2023). Synergistic Association of Hepatitis B Surface Antigen and Plasma Epstein-Barr Virus DNA Load on Distant Metastasis in Patients With Nasopharyngeal Carcinoma. JAMA Network Open. 6(2). e2253832–e2253832. 5 indexed citations
15.
Lin, Kairong, Yabin Jin, Haojiang Li, et al.. (2022). MRI-based radiomics models can improve prognosis prediction for nasopharyngeal carcinoma with neoadjuvant chemotherapy. Magnetic Resonance Imaging. 88. 108–115. 4 indexed citations
16.
Li, Haojiang, Mingyang Chen, Shuqi Li, et al.. (2022). Survival impact of additional induction chemotherapy in nasopharyngeal carcinoma with chronic hepatitis B infection: a retrospective, bi-center study. Annals of Translational Medicine. 10(13). 731–731. 5 indexed citations
17.
Luo, Chao, Shuqi Li, Qin Zhao, et al.. (2022). RuleFit-Based Nomogram Using Inflammatory Indicators for Predicting Survival in Nasopharyngeal Carcinoma, a Bi-Center Study. Journal of Inflammation Research. Volume 15. 4803–4815. 9 indexed citations
18.
Li, Haojiang, Lizhi Liu, Ying Huang, et al.. (2022). Establishment and Validation of a Novel MRI Radiomics Feature-Based Prognostic Model to Predict Distant Metastasis in Endemic Nasopharyngeal Carcinoma. Frontiers in Oncology. 12. 794975–794975. 6 indexed citations
19.
Zhang, Xiang, Zehong Yang, Chushan Zheng, et al.. (2021). Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer. European Radiology. 31(8). 5924–5939. 44 indexed citations
20.
Ke, Chao, Haolin Chen, Xiaofei Lv, et al.. (2019). Differentiation Between Benign and Nonbenign Meningiomas by Using Texture Analysis From Multiparametric MRI. Journal of Magnetic Resonance Imaging. 51(6). 1810–1820. 35 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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