Zhifan Gao

3.7k total citations
111 papers, 2.5k citations indexed

About

Zhifan Gao is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Zhifan Gao has authored 111 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Radiology, Nuclear Medicine and Imaging, 38 papers in Computer Vision and Pattern Recognition and 31 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Zhifan Gao's work include Cardiac Imaging and Diagnostics (23 papers), Coronary Interventions and Diagnostics (20 papers) and Medical Image Segmentation Techniques (18 papers). Zhifan Gao is often cited by papers focused on Cardiac Imaging and Diagnostics (23 papers), Coronary Interventions and Diagnostics (20 papers) and Medical Image Segmentation Techniques (18 papers). Zhifan Gao collaborates with scholars based in China, Canada and Hong Kong. Zhifan Gao's co-authors include Heye Zhang, Shuo Li, Guang Yang, Chenchu Xu, William Kongto Hau, Huahua Xiong, Victor Hugo C. de Albuquerque, Wanqing Wu, Lei Xu and Dhanjoo N. Ghista and has published in prestigious journals such as PLoS ONE, Scientific Reports and Radiology.

In The Last Decade

Zhifan Gao

101 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zhifan Gao China 31 1.1k 818 645 630 414 111 2.5k
Óscar Cámara Spain 28 1.1k 1.0× 606 0.7× 1.4k 2.2× 497 0.8× 220 0.5× 154 3.0k
Wenjia Bai United Kingdom 26 1.3k 1.2× 1.1k 1.4× 801 1.2× 534 0.8× 372 0.9× 91 2.9k
Nabil Ibtehaz Bangladesh 15 795 0.8× 804 1.0× 206 0.3× 425 0.7× 644 1.6× 29 2.2k
Xiahai Zhuang China 27 1.3k 1.2× 1.4k 1.7× 450 0.7× 587 0.9× 605 1.5× 88 2.5k
June‐Goo Lee South Korea 22 1.4k 1.3× 322 0.4× 212 0.3× 670 1.1× 464 1.1× 66 2.3k
Yi Guo China 30 1.5k 1.4× 588 0.7× 132 0.2× 474 0.8× 757 1.8× 163 3.5k
U. Raghavendra India 31 1.2k 1.1× 854 1.0× 406 0.6× 366 0.6× 586 1.4× 84 3.1k
Jean-Louis Coatrieux France 30 1.3k 1.3× 1.9k 2.3× 238 0.4× 1.1k 1.7× 317 0.8× 196 3.7k
Miguel Á. González Ballester Spain 27 629 0.6× 522 0.6× 161 0.2× 707 1.1× 336 0.8× 165 2.4k
S. Kevin Zhou United States 35 1.4k 1.4× 2.6k 3.2× 216 0.3× 990 1.6× 1.1k 2.5× 197 4.5k

Countries citing papers authored by Zhifan Gao

Since Specialization
Citations

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

Fields of papers citing papers by Zhifan Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhifan Gao

This figure shows the co-authorship network connecting the top 25 collaborators of Zhifan Gao. A scholar is included among the top collaborators of Zhifan Gao 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 Zhifan Gao. Zhifan Gao 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.
Wang, Shuai, Ka‐Hou Chan, Yue Sun, et al.. (2025). Multimodal Cross Global Learnable Attention Network for MR images denoising with arbitrary modal missing. Computerized Medical Imaging and Graphics. 121. 102497–102497. 1 indexed citations
2.
Zhou, Zhen, Yifeng Gao, Weiwei Zhang, et al.. (2025). Deep Learning and Radiomics Discrimination of Coronary Chronic Total Occlusion and Subtotal Occlusion using CTA. Academic Radiology. 32(7). 3892–3902.
3.
Gao, Zhifan, et al.. (2025). Multiple token rearrangement Transformer network with explicit superpixel constraint for segmentation of echocardiography. Medical Image Analysis. 101. 103470–103470. 4 indexed citations
4.
Yao, Fengjuan, Chuan Lin, Patrick Cheong‐Iao Pang, et al.. (2025). BLENet: A Bio-Inspired Lightweight and Efficient Network for Left Ventricle Segmentation in Echocardiography. IEEE Transactions on Circuits and Systems for Video Technology. 35(9). 9218–9233. 1 indexed citations
5.
Gao, Zhifan, et al.. (2025). Bi-variational physics-informed operator network for fractional flow reserve curve assessment from coronary angiography. Medical Image Analysis. 103. 103564–103564. 1 indexed citations
6.
Yang, Nan, Giorgos Papanastasiou, Lei Zhu, et al.. (2025). Revisiting medical image retrieval via knowledge consolidation. Medical Image Analysis. 102. 103553–103553.
7.
Gao, Zhifan, et al.. (2025). Causal recurrent intervention for cross-modal cardiac image segmentation. Computerized Medical Imaging and Graphics. 123. 102549–102549. 1 indexed citations
8.
Zhang, David, et al.. (2024). Unsupervised physics-informed deep learning for assessing pulmonary artery hemodynamics. Expert Systems with Applications. 257. 125079–125079. 4 indexed citations
9.
Cho, Yongwon, et al.. (2024). Adaptive dynamic inference for few-shot left atrium segmentation. Medical Image Analysis. 98. 103321–103321. 3 indexed citations
10.
Wu, Weiwen, et al.. (2024). Multi-Level Noise Sampling From Single Image for Low-Dose Tomography Reconstruction. IEEE Journal of Biomedical and Health Informatics. 29(2). 1256–1268.
11.
Lu, Minhua, et al.. (2024). Segmentation-assisted hierarchical constrained state space approach for robust carotid artery wall motion measurement. Expert Systems with Applications. 254. 124377–124377. 2 indexed citations
12.
Wang, Anbang, Yupeng Wang, Shaomin Chen, et al.. (2024). Quantification of functional hemodynamics in aortic valve disease using cardiac computed tomography angiography. Computers in Biology and Medicine. 177. 108608–108608. 1 indexed citations
13.
Liu, Jinhao, Chenchu Xu, Lei Xu, et al.. (2023). Accurate 3D contrast-free myocardial infarction delineation using a 4D dual-stream spatiotemporal feature learning framework. Applied Soft Computing. 146. 110694–110694. 1 indexed citations
14.
Lin, Weiyuan, Zhifan Gao, Hui Liu, & Heye Zhang. (2023). A Deformable Constraint Transport Network for Optimal Aortic Segmentation From CT Images. IEEE Transactions on Medical Imaging. 43(4). 1462–1475. 4 indexed citations
15.
Zhang, Heye, Weifei Wu, Weifei Wu, et al.. (2022). Multi-domain integrative Swin transformer network for sparse-view tomographic reconstruction. Patterns. 3(6). 100498–100498. 47 indexed citations
16.
Zhang, Hong-Wei, Zhifan Gao, Dong Zhang, William Kongto Hau, & Heye Zhang. (2022). Progressive Perception Learning for Main Coronary Segmentation in X-Ray Angiography. IEEE Transactions on Medical Imaging. 42(3). 864–879. 43 indexed citations
17.
Zhang, Heye, Jiaqi Chen, Zhifan Gao, et al.. (2021). Vessel-GAN: Angiographic reconstructions from myocardial CT perfusion with explainable generative adversarial networks. Future Generation Computer Systems. 130. 128–139. 23 indexed citations
18.
Li, Min, Kun Zhang, Zhifan Gao, et al.. (2018). Deep Learning intra-image and inter-images features for Co-saliency detection.. British Machine Vision Conference. 291. 16 indexed citations
19.
Gao, Zhifan, Shanhui Sun, Xin Wang, et al.. (2018). Holistic and Deep Feature Pyramids for Saliency Detection.. British Machine Vision Conference. 67. 15 indexed citations
20.
Pang, Baochuan, et al.. (2010). Cell Nucleus Segmentation in Color Histopathological Imagery Using Convolutional Networks. 1–5. 24 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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