Jinhua Yu

5.9k total citations · 1 hit paper
195 papers, 4.0k citations indexed

About

Jinhua Yu is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Jinhua Yu has authored 195 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 105 papers in Radiology, Nuclear Medicine and Imaging, 53 papers in Computer Vision and Pattern Recognition and 48 papers in Biomedical Engineering. Recurrent topics in Jinhua Yu's work include Radiomics and Machine Learning in Medical Imaging (64 papers), Ultrasound Imaging and Elastography (32 papers) and Medical Image Segmentation Techniques (28 papers). Jinhua Yu is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (64 papers), Ultrasound Imaging and Elastography (32 papers) and Medical Image Segmentation Techniques (28 papers). Jinhua Yu collaborates with scholars based in China, United States and Hong Kong. Jinhua Yu's co-authors include Yuanyuan Wang, Yi Guo, Zeju Li, Zhifeng Shi, Shichong Zhou, Tongtong Liu, Guoqing Wu, Cai Chang, Yao Zhao and Wei Cao and has published in prestigious journals such as Nature Communications, Advanced Functional Materials and Scientific Reports.

In The Last Decade

Jinhua Yu

182 papers receiving 3.9k citations

Hit Papers

Deep learning radiomics can predict axillary lymph node s... 2020 2026 2022 2024 2020 100 200 300 400
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. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer
    2020 402 citations Xueyi Zheng, Yao Zhao et al. Nature Communications profile →

Peers

Jinhua Yu
Michael Pringle United Kingdom
Zeynettin Akkus United States
Pierre Véra France
Jeon‐Hor Chen Taiwan
Arkadiusz Gertych United States
Michael R. Kaus United States
Andrzej Materka Poland
Timothy L. Kline United States
Zaiyi Liu China
Joost J. M. van Griethuysen Netherlands
Michael Pringle United Kingdom
Jinhua Yu
Citations per year, relative to Jinhua Yu Jinhua Yu (= 1×) peers Michael Pringle

Countries citing papers authored by Jinhua Yu

Since Specialization
Citations

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

Fields of papers citing papers by Jinhua Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jinhua Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Jinhua Yu. A scholar is included among the top collaborators of Jinhua Yu 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 Jinhua Yu. Jinhua Yu 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.
Song, Pengfei, Yan Li, Zhiyong Shao, et al.. (2025). An AI-assisted fluorescence microscopic system for screening mitophagy inducers by simultaneous analysis of mitophagic intermediates. Nature Communications. 16(1). 5179–5179. 3 indexed citations
2.
Yu, Jinhua, Dairong Chen, Yi Lin, et al.. (2025). Fine-grained building function recognition with street-view images and GIS map data via geometry-aware semi-supervised learning. International Journal of Applied Earth Observation and Geoinformation. 137. 104386–104386. 2 indexed citations
3.
Ning, Z., et al.. (2025). Dual-path neural network extracts tumor microenvironment information from whole slide images to predict molecular typing and prognosis of Glioma. Computer Methods and Programs in Biomedicine. 261. 108580–108580. 1 indexed citations
4.
Wu, Yonghuang, et al.. (2025). Adaptive Multi-Scale Dynamic Graph Representation Learning With Overlapping Community-Awareness for ASD Classification. IEEE Journal of Biomedical and Health Informatics. 29(12). 8711–8718.
5.
Chen, Sihui, Pengcheng Zhang, Shiming Zhou, et al.. (2025). Label-free navigation system for grading prostate tumour malignancy in situ via tissue pH and prostate-specific antigen activity. Nature Biomedical Engineering.
6.
Wang, Yuanyuan, et al.. (2024). Integrated diagnosis of glioma based on magnetic resonance images with incomplete ground truth labels. Computers in Biology and Medicine. 180. 108968–108968. 3 indexed citations
7.
Ni, Yi‐Qing, et al.. (2024). Active learning based on multi-enhanced views for classification of multiple patterns in lung ultrasound images. Computerized Medical Imaging and Graphics. 118. 102454–102454. 2 indexed citations
8.
Liang, Yingyu, Yi Ren, Jinhua Yu, & Wenting Zha. (2023). Current trajectory image-based protection algorithm for transmission lines connected to MMC-HVDC stations using CA-CNN. Protection and Control of Modern Power Systems. 8(1). 15 indexed citations
9.
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Li, Weijia, Wenqian Zhao, Jinhua Yu, et al.. (2023). Joint semantic–geometric learning for polygonal building segmentation from high-resolution remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing. 201. 26–37. 36 indexed citations
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Jiao, Jing, et al.. (2023). RsALUNet: A reinforcement supervision U-Net-based framework for multi-ROI segmentation of medical images. Biomedical Signal Processing and Control. 84. 104743–104743. 2 indexed citations
14.
Wu, Guoqing, Xitian Fan, Xuan Feng, et al.. (2021). MRI-based brain tumor segmentation using FPGA-accelerated neural network. BMC Bioinformatics. 22(1). 421–421. 33 indexed citations
15.
Yu, Jinhua, Yinhui Deng, Tongtong Liu, et al.. (2020). Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics. Nature Communications. 11(1). 4807–4807. 200 indexed citations
16.
Zheng, Xueyi, Yao Zhao, Yini Huang, et al.. (2020). Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nature Communications. 11(1). 1236–1236. 402 indexed citations breakdown →
17.
Li, Jiawei, Zhou Fang, Jin Zhou, et al.. (2019). The association between molecular biomarkers and ultrasonographic radiomics features for triple negative invasive breast carcinoma. Zhonghua chaosheng yingxiangxue zazhi. 28(2). 137–143. 2 indexed citations
18.
Zhao, Jinxin, et al.. (2015). Subarray coherence based postfilter for eigenspace based minimum variance beamformer in ultrasound plane-wave imaging. Ultrasonics. 65. 23–33. 47 indexed citations
19.
Yu, Jinhua, et al.. (2015). Enhance contrast in PCA based beamformers using smoothing kernel. Bio-Medical Materials and Engineering. 26(1_suppl). S1613–21.
20.
Deng, Yinhui, et al.. (2014). Evaluation of fatty proportion in fatty liver using least squares method with constraints. Bio-Medical Materials and Engineering. 24(6). 2811–2820. 2 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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