Fengjun Zhao

852 total citations
65 papers, 634 citations indexed

About

Fengjun Zhao is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Fengjun Zhao has authored 65 papers receiving a total of 634 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Radiology, Nuclear Medicine and Imaging, 20 papers in Biomedical Engineering and 15 papers in Computer Vision and Pattern Recognition. Recurrent topics in Fengjun Zhao's work include Optical Imaging and Spectroscopy Techniques (20 papers), Photoacoustic and Ultrasonic Imaging (15 papers) and Radiomics and Machine Learning in Medical Imaging (14 papers). Fengjun Zhao is often cited by papers focused on Optical Imaging and Spectroscopy Techniques (20 papers), Photoacoustic and Ultrasonic Imaging (15 papers) and Radiomics and Machine Learning in Medical Imaging (14 papers). Fengjun Zhao collaborates with scholars based in China, United States and Australia. Fengjun Zhao's co-authors include Xiaowei He, Yuqing Hou, Xin Cao, Jimin Liang, Linyu Yang, Yanrong Chen, Huangjian Yi, Dongmei Chen, Jun Liu and Peng Gong and has published in prestigious journals such as Applied Physics Letters, Journal of Applied Physics and Chemical Engineering Journal.

In The Last Decade

Fengjun Zhao

58 papers receiving 613 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fengjun Zhao China 15 264 142 130 129 78 65 634
Wen Guo China 17 164 0.6× 64 0.5× 106 0.8× 257 2.0× 112 1.4× 72 979
Giorgos Papanastasiou United Kingdom 15 255 1.0× 116 0.8× 174 1.3× 66 0.5× 153 2.0× 53 742
Lin Huang China 19 179 0.7× 726 5.1× 75 0.6× 211 1.6× 21 0.3× 124 1.3k
Yuemeng Li China 11 138 0.5× 71 0.5× 41 0.3× 92 0.7× 48 0.6× 42 450
Chengjia Wang United Kingdom 12 164 0.6× 74 0.5× 158 1.2× 22 0.2× 159 2.0× 28 541
Zhihao Wu China 9 429 1.6× 185 1.3× 341 2.6× 55 0.4× 343 4.4× 16 1.0k
Matthew Field Australia 14 310 1.2× 154 1.1× 95 0.7× 25 0.2× 192 2.5× 43 756
Mo Tao China 11 87 0.3× 257 1.8× 91 0.7× 33 0.3× 139 1.8× 52 766
Weiwei Wu China 18 311 1.2× 228 1.6× 158 1.2× 122 0.9× 163 2.1× 36 877
Minghui Wang China 18 366 1.4× 175 1.2× 401 3.1× 140 1.1× 425 5.4× 54 1.3k

Countries citing papers authored by Fengjun Zhao

Since Specialization
Citations

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

Fields of papers citing papers by Fengjun Zhao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fengjun Zhao

This figure shows the co-authorship network connecting the top 25 collaborators of Fengjun Zhao. A scholar is included among the top collaborators of Fengjun Zhao 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 Fengjun Zhao. Fengjun Zhao 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.
Sun, Zhipeng, et al.. (2024). CORONet: A Cross-Sequence Joint Representation and Hypergraph Convolutional Network for Classifying Molecular Subtypes of Breast Cancer Using Incomplete DCE-MRI. IEEE Journal of Biomedical and Health Informatics. 28(4). 2103–2114. 3 indexed citations
2.
Ma, Mingze, Weibo Gao, Xiaowei He, et al.. (2024). A Deep Learning Model for Predicting Molecular Subtype of Breast Cancer by Fusing Multiple Sequences of DCE-MRI From Two Institutes. Academic Radiology. 31(9). 3479–3488. 6 indexed citations
3.
Han, Xinxin, et al.. (2024). PointCluster: Deep Clustering of 3-D Point Clouds With Semantic Pseudo-Labeling. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–14. 2 indexed citations
4.
Zhao, Fengjun, Zhiwei Wang, Hongyan Du, Xiaowei He, & Xin Cao. (2023). Self-Supervised Triplet Contrastive Learning for Classifying Endometrial Histopathological Images. IEEE Journal of Biomedical and Health Informatics. 27(12). 5970–5981. 8 indexed citations
5.
6.
Li, Renpeng, et al.. (2023). Epidemiology of human papillomavirus on condyloma acuminatum in Shandong Province，China. Human Vaccines & Immunotherapeutics. 19(1). 2170662–2170662. 10 indexed citations
7.
Chen, Yi, Kang Li, Fengjun Zhao, et al.. (2023). Regularized reconstruction based on joint smoothly clipped absolute deviation regularization and graph manifold learning for fluorescence molecular tomography. Physics in Medicine and Biology. 68(19). 195004–195004.
8.
9.
Gao, Weibo, Jixin Chen, Bin Zhang, et al.. (2023). Automatic deep learning method for detection and classification of breast lesions in dynamic contrast-enhanced magnetic resonance imaging. Quantitative Imaging in Medicine and Surgery. 13(4). 2620–2633. 2 indexed citations
10.
Chen, Yi, Linzhi Su, Huangjian Yi, et al.. (2022). ABPO-TVSCAD: alternating Bregman proximity operators approach based on TVSCAD regularization for bioluminescence tomography. Physics in Medicine and Biology. 67(21). 215013–215013. 4 indexed citations
11.
Yang, Lijuan, Fengjun Zhao, Dong Wang, et al.. (2022). A deep learning model combining multimodal radiomics, clinical and imaging features for differentiating ocular adnexal lymphoma from idiopathic orbital inflammation. European Radiology. 32(10). 6922–6932. 27 indexed citations
12.
Hou, Yuqing, Jixin Chen, Peng Lv, et al.. (2020). Bag-of-features-based radiomics for differentiation of ocular adnexal lymphoma and idiopathic orbital inflammation from contrast-enhanced MRI. European Radiology. 31(1). 24–33. 24 indexed citations
13.
Zhao, Fengjun, et al.. (2019). Accurate Segmentation of Heart Volume in CTA With Landmark-Based Registration and Fully Convolutional Network. IEEE Access. 7. 57881–57893. 5 indexed citations
14.
Zhao, Fengjun, Yibing Chen, Fei Chen, et al.. (2018). Semi-Supervised Cerebrovascular Segmentation by Hierarchical Convolutional Neural Network. IEEE Access. 6. 67841–67852. 24 indexed citations
15.
Hu, Yuelin, Huangjian Yi, Xin Cao, et al.. (2018). Recognition for multiple sources of bioluminescence tomography: a comparative study. 48–48. 1 indexed citations
16.
Zhao, Fengjun, Bin Wu, Fei Chen, et al.. (2018). An automatic multi-class coronary atherosclerosis plaque detection and classification framework. Medical & Biological Engineering & Computing. 57(1). 245–257. 15 indexed citations
17.
Zhao, Fengjun, Yuqing Hou, Yanrong Chen, et al.. (2017). A monocentric centerline extraction method for ring-like blood vessels. Medical & Biological Engineering & Computing. 56(4). 695–707. 1 indexed citations
18.
Yi, Huangjian, Xu Zhang, Jinye Peng, et al.. (2016). Reconstruction for Limited-Projection Fluorescence Molecular Tomography Based on a Double-Mesh Strategy. BioMed Research International. 2016. 1–11. 9 indexed citations
19.
Ma, Xiaodong, Mengying Zhao, Fengjun Zhao, et al.. (2016). Application of silica-based monolith as solid-phase extraction sorbent for extracting toxaphene congeners in soil. Journal of Sol-Gel Science and Technology. 80(1). 87–95. 4 indexed citations
20.
Zhao, Fengjun, Jimin Liang, Dongmei Chen, et al.. (2015). Automatic segmentation method for bone and blood vessel in murine hindlimb. Medical Physics. 42(7). 4043–4054. 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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