Zhe Liu

1.6k total citations · 1 hit paper
66 papers, 1.0k citations indexed

About

Zhe Liu is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Zhe Liu has authored 66 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Computer Vision and Pattern Recognition, 29 papers in Radiology, Nuclear Medicine and Imaging and 24 papers in Artificial Intelligence. Recurrent topics in Zhe Liu's work include Advanced Neural Network Applications (25 papers), Medical Image Segmentation Techniques (20 papers) and COVID-19 diagnosis using AI (16 papers). Zhe Liu is often cited by papers focused on Advanced Neural Network Applications (25 papers), Medical Image Segmentation Techniques (20 papers) and COVID-19 diagnosis using AI (16 papers). Zhe Liu collaborates with scholars based in China, United States and United Kingdom. Zhe Liu's co-authors include Yuqing Song, Victor S. Sheng, Kai Han, Liangmin Wang, Xiaolin Zhang, Rui Jiang, Yang Lu, Renaud Marlet, Yan Zhu and Lei Wu and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and IEEE Transactions on Medical Imaging.

In The Last Decade

Zhe Liu

61 papers receiving 990 citations

Hit Papers

Deep semi-supervised learning for medical image segmentat... 2024 2026 2025 2024 25 50 75
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 semi-supervised learning for medical image segmentation: A review
    2024 75 citations Kai Han, Victor S. Sheng et al. Expert Systems with Applications profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zhe Liu China 17 523 367 342 136 124 66 1.0k
Şaban Öztürk Türkiye 23 537 1.0× 456 1.2× 570 1.7× 107 0.8× 140 1.1× 63 1.6k
Dazhe Zhao China 22 463 0.9× 643 1.8× 423 1.2× 155 1.1× 111 0.9× 146 1.5k
Yuqing Song China 21 597 1.1× 279 0.8× 358 1.0× 101 0.7× 119 1.0× 138 1.3k
Junding Sun China 18 612 1.2× 320 0.9× 462 1.4× 358 2.6× 143 1.2× 90 1.5k
N. Sri Madhava Raja India 16 488 0.9× 543 1.5× 464 1.4× 239 1.8× 139 1.1× 46 1.3k
Keh-Shih Chuang Taiwan 11 700 1.3× 293 0.8× 273 0.8× 140 1.0× 111 0.9× 24 1.3k
Mingfeng Jiang China 17 324 0.6× 299 0.8× 248 0.7× 71 0.5× 156 1.3× 136 1.1k
Xi Wu China 21 686 1.3× 732 2.0× 368 1.1× 189 1.4× 240 1.9× 79 1.6k
Zizhao Zhang United States 19 789 1.5× 421 1.1× 824 2.4× 67 0.5× 98 0.8× 42 1.6k
Walid Barhoumi Tunisia 18 460 0.9× 210 0.6× 414 1.2× 67 0.5× 75 0.6× 99 965

Countries citing papers authored by Zhe Liu

Since Specialization
Citations

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

Fields of papers citing papers by Zhe Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhe Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Zhe Liu. A scholar is included among the top collaborators of Zhe Liu 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 Zhe Liu. Zhe Liu 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.
Han, Kai, et al.. (2025). Region Uncertainty Estimation for Medical Image Segmentation With Noisy Labels. IEEE Transactions on Medical Imaging. 44(12). 5197–5207.
2.
Liu, Zhe, Yuqing Song, Wenfeng Hao, et al.. (2024). HI-Net: Liver vessel segmentation with hierarchical inter-scale multi-scale feature fusion. Biomedical Signal Processing and Control. 96. 106604–106604. 6 indexed citations
3.
Li, Yuefeng, et al.. (2024). MAR-GAN: Multi attention residual generative adversarial network for tumor segmentation in breast ultrasounds. Biomedical Signal Processing and Control. 100. 107171–107171. 2 indexed citations
4.
Han, Kai, et al.. (2024). Deep semi-supervised learning for medical image segmentation: A review. Expert Systems with Applications. 245. 123052–123052. 75 indexed citations breakdown →
5.
Liu, Zhe, et al.. (2024). Tutor Assisted Feature Distillation. 1–6.
6.
Song, Yuqing, et al.. (2023). MMMViT: Multiscale multimodal vision transformer for brain tumor segmentation with missing modalities. Biomedical Signal Processing and Control. 90. 105827–105827. 6 indexed citations
7.
Liu, Zhe, et al.. (2023). Improved Faster R-CNN Traffic Sign Detection Algorithm Based on Transformer. Journal of Physics Conference Series. 2637(1). 12041–12041. 1 indexed citations
8.
Rong, Huan, et al.. (2022). Cascaded multi-point regression Network for high-quality generic lesion detection. Expert Systems with Applications. 214. 119141–119141. 1 indexed citations
9.
Liu, Zhe, et al.. (2022). A survey on the interpretability of deep learning in medical diagnosis. Multimedia Systems. 28(6). 2335–2355. 92 indexed citations
10.
Cao, Min‐Jie, et al.. (2021). Research for Face Image Super-Resolution Reconstruction Based on Wavelet Transform and SRGAN. 448–451. 12 indexed citations
11.
Bu, Jiajun, et al.. (2021). Efficient Medical Image Segmentation Based on Knowledge Distillation. IEEE Transactions on Medical Imaging. 40(12). 3820–3831. 121 indexed citations
12.
Liu, Zhe, et al.. (2021). DV-Net: Accurate liver vessel segmentation via dense connection model with D-BCE loss function. Knowledge-Based Systems. 232. 107471–107471. 52 indexed citations
13.
Wei, Juan, et al.. (2020). Abnormal Acoustic Event Detection Based on Orthogonal Matching Pursuit in Security Surveillance System. Wireless Personal Communications. 114(2). 1009–1024. 3 indexed citations
14.
Liu, Zhe, Kai Han, Zhaohui Wang, et al.. (2020). Automatic liver segmentation from abdominal CT volumes using improved convolution neural networks. Multimedia Systems. 27(1). 111–124. 24 indexed citations
15.
Liu, Zhe, Yuqing Song, Victor S. Sheng, et al.. (2019). Liver CT sequence segmentation based with improved U-Net and graph cut. Expert Systems with Applications. 126. 54–63. 127 indexed citations
16.
Song, Yuqing, et al.. (2019). Advances on pancreas segmentation: a review. Multimedia Tools and Applications. 79(9-10). 6799–6821. 21 indexed citations
17.
Liu, Zhe, et al.. (2019). MRI and PET image fusion using the nonparametric density model and the theory of variable-weight. Computer Methods and Programs in Biomedicine. 175. 73–82. 28 indexed citations
18.
Liu, Zhe, et al.. (2019). Texture Feature Extraction from Thyroid MR Imaging Using High-Order Derived Mean CLBP. Journal of Computer Science and Technology. 34(1). 35–46. 3 indexed citations
19.
20.
Liu, Zhe, Xiang Deng, & Guangzhi Wang. (2012). Accuracy Validation for Medical Image Registration Algorithms: a Review. Chinese Medical Sciences Journal. 27(3). 176–181. 11 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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