Zhichao Feng

2.9k total citations · 1 hit paper
88 papers, 1.8k citations indexed

About

Zhichao Feng is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Surgery. According to data from OpenAlex, Zhichao Feng has authored 88 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Radiology, Nuclear Medicine and Imaging, 25 papers in Biomedical Engineering and 16 papers in Surgery. Recurrent topics in Zhichao Feng's work include Radiomics and Machine Learning in Medical Imaging (21 papers), Pancreatic function and diabetes (9 papers) and Graphene and Nanomaterials Applications (8 papers). Zhichao Feng is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (21 papers), Pancreatic function and diabetes (9 papers) and Graphene and Nanomaterials Applications (8 papers). Zhichao Feng collaborates with scholars based in China, Canada and United States. Zhichao Feng's co-authors include Pengfei Rong, Wei Wang, Qianyun Liu, Hong Liu, Rennian Wang, Mengtian Ma, Matthew Riopel, Chunhui Sun, Ze Mi and Zhimin Yan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nano Letters and ACS Nano.

In The Last Decade

Zhichao Feng

82 papers receiving 1.8k citations

Hit Papers

Engineered MXene Biomaterials for Regenerative Medicine 2025 2026 2025 5 10 15 20
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.

  1. Engineered MXene Biomaterials for Regenerative Medicine
    2025 24 citations Shengmin Zhang, Liang Wang et al. ACS Nano profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zhichao Feng China 24 541 517 315 300 197 88 1.8k
Yiming Li China 27 884 1.6× 262 0.5× 689 2.2× 458 1.5× 141 0.7× 87 2.7k
Zhenghong Lee United States 25 341 0.6× 294 0.6× 513 1.6× 271 0.9× 200 1.0× 84 1.7k
Jian Fu China 18 258 0.5× 381 0.7× 604 1.9× 113 0.4× 175 0.9× 122 1.8k
Karun Sharma United States 27 377 0.7× 943 1.8× 255 0.8× 371 1.2× 453 2.3× 88 2.2k
Xuening Zhang China 25 163 0.3× 692 1.3× 369 1.2× 278 0.9× 73 0.4× 101 2.0k
Yijen Wu United States 21 422 0.8× 360 0.7× 415 1.3× 115 0.4× 249 1.3× 66 1.8k
Rene Schloss United States 21 124 0.2× 539 1.0× 729 2.3× 91 0.3× 507 2.6× 58 2.6k
Soo‐Yeon Kim South Korea 21 665 1.2× 308 0.6× 301 1.0× 191 0.6× 167 0.8× 105 1.9k
Dov Hershkovitz Israel 26 209 0.4× 236 0.5× 647 2.1× 428 1.4× 272 1.4× 95 2.2k
Joon‐Young Kim South Korea 29 754 1.4× 119 0.2× 596 1.9× 307 1.0× 266 1.4× 110 2.5k

Countries citing papers authored by Zhichao Feng

Since Specialization
Citations

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

Fields of papers citing papers by Zhichao Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhichao Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Zhichao Feng. A scholar is included among the top collaborators of Zhichao Feng 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 Zhichao Feng. Zhichao Feng 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.
Huang, Haina, Kai Bian, Zhichao Feng, et al.. (2025). Programmatic regulation of macrophage polarization by HAp@MXene nanocomposites to promote bone repair. SHILAP Revista de lepidopterología. 4(1).
2.
Zhao, Xu, Hui Wang, Zhichao Feng, et al.. (2025). Design and simulation of a 7.0 T conduction cooled superconducting magnet. Scientific Reports. 15(1). 15699–15699.
3.
Zhang, Shengmin, Liang Wang, Zhichao Feng, et al.. (2025). Engineered MXene Biomaterials for Regenerative Medicine. ACS Nano. 19(10). 9590–9635. 24 indexed citations breakdown →
4.
Feng, Zhichao, Mengtian Ma, Fang Shao, et al.. (2024). Different impacts of adipose tissue dynamics on prognosis in patients with resectable locally advanced rectal cancer treated with and without neoadjuvant treatment. Frontiers in Oncology. 14. 1421651–1421651. 1 indexed citations
5.
Qu, Hongyi, et al.. (2024). Thermal Analysis of the 4 K Cryostat Within a 1.5 T Conduction-Cooled Superconducting Magnet for Whole-Body MRI Application. IEEE Transactions on Applied Superconductivity. 34(8). 1–5. 1 indexed citations
6.
Wang, Lei, et al.. (2024). Design and analysis of mechanical structure for on-board HTS magnets subjected to high acceleration. The European Physical Journal Special Topics. 2 indexed citations
7.
Zhang, Wenlan, et al.. (2023). Professional development for STEM educators: A bibliometric analysis of the recent progress. Review of Education. 11(1). 2 indexed citations
8.
Zeng, Ling‐Li, Kai Gao, Dewen Hu, et al.. (2023). SS-TBN: A Semi-Supervised Tri-Branch Network for COVID-19 Screening and Lesion Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(8). 10427–10442. 21 indexed citations
9.
Lian, Wang, et al.. (2023). DHUnet: Dual-branch hierarchical global–local fusion network for whole slide image segmentation. Biomedical Signal Processing and Control. 85. 104976–104976. 19 indexed citations
10.
Feng, Zhichao, et al.. (2022). A review of machine learning approaches, challenges and prospects for computational tumor pathology. arXiv (Cornell University). 2 indexed citations
11.
12.
Gao, Kai, Jianpo Su, Zhichao Feng, et al.. (2020). Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images. Medical Image Analysis. 67. 101836–101836. 136 indexed citations
13.
14.
15.
Zheng, Wei, Qi Liang, Huasheng Liu, et al.. (2020). Volume changes of subcortical structures in patients with post-hepatitis B cirrhosis: A magnetic resonance imaging study.. PubMed. 45(9). 1109–1114. 1 indexed citations
16.
17.
Liu, Jun, Limin Peng, Zhichao Feng, et al.. (2018). Changes in default mode network connectivity in different glucose metabolism status and diabetes duration. NeuroImage Clinical. 21. 101629–101629. 32 indexed citations
18.
Yan, Zhimin, Zhichao Feng, Peng Cao, & Pengfei Rong. (2017). Value of multi-slice spiral CT image texture analysis in diagnosing lymphatic metastasis of rectal cancer. Zhonghua fangshexian yixue zazhi. 51(6). 432–436.
19.
Feng, Zhichao, Pengfei Rong, Peng Cao, et al.. (2017). Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma. European Radiology. 28(4). 1625–1633. 167 indexed citations
20.
Feng, Zhichao, et al.. (2012). Inhibition of Gsk3β activity improves β-cell function in c-Kit male mice. Laboratory Investigation. 92(4). 543–555. 37 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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