Wuchao Li

422 total citations
24 papers, 252 citations indexed

About

Wuchao Li is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Wuchao Li has authored 24 papers receiving a total of 252 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Radiology, Nuclear Medicine and Imaging, 12 papers in Pulmonary and Respiratory Medicine and 6 papers in Biomedical Engineering. Recurrent topics in Wuchao Li's work include Radiomics and Machine Learning in Medical Imaging (14 papers), Advanced X-ray and CT Imaging (6 papers) and AI in cancer detection (4 papers). Wuchao Li is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (14 papers), Advanced X-ray and CT Imaging (6 papers) and AI in cancer detection (4 papers). Wuchao Li collaborates with scholars based in China, United States and France. Wuchao Li's co-authors include Jie Tian, Liwen Zhang, Mengjie Fang, Rongpin Wang, Di Dong, Zaiyi Liu, Xiaohan Hao, Shuo Wang, Chong Tian and Junlin Zhou and has published in prestigious journals such as PLoS ONE, Journal of Ethnopharmacology and Medical Physics.

In The Last Decade

Wuchao Li

18 papers receiving 247 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wuchao Li China 9 165 124 49 39 33 24 252
Carsten Ammitzbøl Lauridsen Denmark 10 264 1.6× 149 1.2× 28 0.6× 102 2.6× 47 1.4× 25 369
Atsushi Goto Japan 8 77 0.5× 84 0.7× 43 0.9× 19 0.5× 63 1.9× 29 295
Zhou Dong China 3 89 0.5× 41 0.3× 42 0.9× 63 1.6× 25 0.8× 6 153
Xiaoming Zhou China 10 223 1.4× 75 0.6× 45 0.9× 45 1.2× 34 1.0× 24 323
Yehang Chen China 10 318 1.9× 174 1.4× 36 0.7× 76 1.9× 81 2.5× 26 382
Athanasia Mitsala Greece 4 79 0.5× 46 0.4× 106 2.2× 39 1.0× 20 0.6× 11 223
Bao Feng China 10 345 2.1× 202 1.6× 40 0.8× 80 2.1× 85 2.6× 32 413
Yingqian Ge China 12 274 1.7× 137 1.1× 61 1.2× 21 0.5× 119 3.6× 22 330

Countries citing papers authored by Wuchao Li

Since Specialization
Citations

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

Fields of papers citing papers by Wuchao Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wuchao Li

This figure shows the co-authorship network connecting the top 25 collaborators of Wuchao Li. A scholar is included among the top collaborators of Wuchao 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 Wuchao Li. Wuchao 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.
Liu, Feng, Ying Cao, Wuchao Li, et al.. (2025). PcPreT-Net: Predicting classification of decline rate in prostate-specific antigen using graph neural network. Displays. 90. 103164–103164.
2.
Liang, Hengrui, Wei Wang, Wuchao Li, et al.. (2025). A vision–language pretrained transformer for versatile clinical respiratory disease applications. Nature Biomedical Engineering. 1 indexed citations
3.
Li, Wuchao, et al.. (2025). RPF-Net: A multimodal model for the postoperative UISS risk stratification of non-metastatic ccRCC based on CT and whole-slide images. Computer Methods and Programs in Biomedicine. 268. 108836–108836.
4.
Li, Wuchao, et al.. (2025). HGTL: A hypergraph transfer learning framework for survival prediction of ccRCC. Medical Image Analysis. 105. 103700–103700.
5.
Li, Wuchao, et al.. (2025). GAMMIL: A graph attention-guided multi-scale fusion multiple instance learning model for the WHO grading of meningioma in whole slide images. Biomedical Signal Processing and Control. 105. 107652–107652.
6.
Li, Wuchao, Shasha Zhang, Jianguo Zhu, et al.. (2024). Multicenter evaluation of CT deep radiomics model in predicting Leibovich score risk groups for non-metastatic clear cell renal cell carcinoma. Displays. 85. 102867–102867. 2 indexed citations
7.
Zhang, Mudan, Xuntao Yin, Wuchao Li, et al.. (2023). A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly. BMC Medical Imaging. 23(1). 181–181. 1 indexed citations
8.
Liu, Xiao, Rui Xiang, Kun Lv, et al.. (2023). Development and validation of a radiomics-based prediction pipeline for the response to stereotactic radiosurgery therapy in brain metastases. European Radiology. 33(12). 8925–8935. 8 indexed citations
9.
Li, Wuchao, Yunsong Peng, Lihui Wang, et al.. (2023). TGMIL: A hybrid multi-instance learning model based on the Transformer and the Graph Attention Network for whole-slide images classification of renal cell carcinoma. Computer Methods and Programs in Biomedicine. 242. 107789–107789. 15 indexed citations
10.
Li, Wuchao, et al.. (2023). Case Report: Multidisciplinary management of primary inferior vena cava leiomyosarcoma: a comprehensive case study. Frontiers in Oncology. 13. 1190276–1190276. 1 indexed citations
11.
Jiang, Kehua, Rongpin Wang, Wuchao Li, et al.. (2023). Root extract of Hemsleya amabilis Diels suppresses renal cell carcinoma cell growth through inducing apoptosis and G2/M phase arrest via PI3K/AKT signaling pathway. Journal of Ethnopharmacology. 318(Pt B). 117014–117014. 6 indexed citations
12.
Zhang, Mudan, et al.. (2023). SaB-Net: Self-attention backward network for gastric tumor segmentation in CT images. Computers in Biology and Medicine. 169. 107866–107866. 9 indexed citations
13.
Jiang, Yi, Wuchao Li, Chencui Huang, et al.. (2020). Preoperative CT Radiomics Predicting the SSIGN Risk Groups in Patients With Clear Cell Renal Cell Carcinoma: Development and Multicenter Validation. Frontiers in Oncology. 10. 909–909. 8 indexed citations
14.
Zhang, Liwen, Di Dong, Wenjuan Zhang, et al.. (2020). A deep learning risk prediction model for overall survival in patients with gastric cancer: A multicenter study. Radiotherapy and Oncology. 150. 73–80. 69 indexed citations
15.
Jiang, Yi, Wuchao Li, Chencui Huang, et al.. (2020). A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma. Frontiers in Oncology. 10. 592–592. 5 indexed citations
16.
Ye, Chen, Yongbin Qin, Lihui Wang, et al.. (2020). Accurate intravoxel incoherent motion parameter estimation using Bayesian fitting and reduced number of low b‐values. Medical Physics. 47(9). 4372–4385. 6 indexed citations
17.
Qin, Yongbin, et al.. (2019). Estimation of intravoxel incoherent motion parameters using low b-values. PLoS ONE. 14(2). e0211911–e0211911. 12 indexed citations
18.
Li, Wuchao, Liwen Zhang, Chong Tian, et al.. (2018). Prognostic value of computed tomography radiomics features in patients with gastric cancer following curative resection. European Radiology. 29(6). 3079–3089. 68 indexed citations
19.
20.
Ma, Xiaoqiong, et al.. (2013). Determination and pharmacokinetic study of the diacid metabolite of norcantharidin in beagle plasma by use of liquid chromatography–tandem mass spectrometry. Analytical and Bioanalytical Chemistry. 405(28). 9273–9283. 8 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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