Ruogu Fang

3.9k total citations · 1 hit paper
71 papers, 1.6k citations indexed

About

Ruogu Fang is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Ruogu Fang has authored 71 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Radiology, Nuclear Medicine and Imaging, 22 papers in Computer Vision and Pattern Recognition and 11 papers in Artificial Intelligence. Recurrent topics in Ruogu Fang's work include Retinal Imaging and Analysis (13 papers), Advanced MRI Techniques and Applications (12 papers) and Medical Imaging Techniques and Applications (11 papers). Ruogu Fang is often cited by papers focused on Retinal Imaging and Analysis (13 papers), Advanced MRI Techniques and Applications (12 papers) and Medical Imaging Techniques and Applications (11 papers). Ruogu Fang collaborates with scholars based in United States, China and Hong Kong. Ruogu Fang's co-authors include Tsuhan Chen, Skylar E. Stolte, Pina C. Sanelli, Noah Snavely, Kevin Tang, Huating Li, Weiping Jia, Qiang Wu, Bin Sheng and Samira Pouyanfar and has published in prestigious journals such as SHILAP Revista de lepidopterología, NeuroImage and Scientific Reports.

In The Last Decade

Ruogu Fang

62 papers receiving 1.5k citations

Hit Papers

A Comprehensive Survey of Foundation Models in Medicine 2025 2026 2025 5 10 15
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. A Comprehensive Survey of Foundation Models in Medicine
    2025 16 citations Wasif Khan, Joshua K. Wong et al. IEEE Reviews in Biomedical Engineering profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ruogu Fang United States 18 662 590 307 273 181 71 1.6k
Beiji Zou China 24 802 1.2× 1.2k 2.0× 499 1.6× 324 1.2× 166 0.9× 189 2.1k
Bin Sheng China 24 611 0.9× 1.0k 1.8× 275 0.9× 348 1.3× 142 0.8× 114 2.1k
Muhammad Arsalan South Korea 26 802 1.2× 956 1.6× 437 1.4× 353 1.3× 107 0.6× 76 1.9k
Anas Bilal China 18 492 0.7× 324 0.5× 180 0.6× 302 1.1× 50 0.3× 66 1.1k
Fahmi Khalifa United States 26 1.4k 2.1× 831 1.4× 200 0.7× 517 1.9× 379 2.1× 153 2.5k
Marcos Ortega Spain 24 1.2k 1.8× 461 0.8× 735 2.4× 278 1.0× 302 1.7× 151 1.9k
Manuel G. Penedo Spain 23 1.0k 1.6× 594 1.0× 751 2.4× 273 1.0× 204 1.1× 121 1.9k
Andrew Hunter United Kingdom 22 1.3k 2.0× 1.1k 1.9× 1.1k 3.6× 404 1.5× 166 0.9× 142 2.5k
Dwarikanath Mahapatra Switzerland 23 905 1.4× 1.1k 1.8× 352 1.1× 464 1.7× 207 1.1× 87 1.8k
Sulatha V. Bhandary India 27 2.0k 3.0× 1.1k 1.8× 1.6k 5.2× 197 0.7× 165 0.9× 71 2.5k

Countries citing papers authored by Ruogu Fang

Since Specialization
Citations

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

Fields of papers citing papers by Ruogu Fang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ruogu Fang

This figure shows the co-authorship network connecting the top 25 collaborators of Ruogu Fang. A scholar is included among the top collaborators of Ruogu Fang 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 Ruogu Fang. Ruogu Fang 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.
Liu, Junyan, Ruogu Fang, Qingyue Wei, et al.. (2025). Revealing neurocognitive and behavioral patterns through unsupervised manifold learning of dynamic brain data. Nature Computational Science. 5(12). 1238–1252.
2.
Khan, Wasif, et al.. (2025). A Comprehensive Survey of Foundation Models in Medicine. IEEE Reviews in Biomedical Engineering. 19. 283–304. 16 indexed citations breakdown →
3.
Indahlastari, Aprinda, Alejandro Albizu, Skylar E. Stolte, Ruogu Fang, & Adam J. Woods. (2025). Practical considerations in modeling tDCS in older adults. Brain stimulation. 18(1). 332–333.
4.
Liu, Mengting, et al.. (2025). Exploring the effect of gestational diabetes mellitus on retinal vascular morphology by PKSEA-Net. Frontiers in Cell and Developmental Biology. 12. 1532939–1532939.
5.
Acharya, Celin, et al.. (2025). TRACE: applying AI language models to extract ancestry information from curated biomedical literature. Frontiers in Digital Health. 7. 1608370–1608370.
6.
Stolte, Skylar E., et al.. (2024). BrainSegFounder: Towards 3D foundation models for neuroimage segmentation. Medical Image Analysis. 97. 103301–103301. 14 indexed citations
7.
Albizu, Alejandro, Aprinda Indahlastari, Paulo Suen, et al.. (2024). Machine learning-optimized non-invasive brain stimulation and treatment response classification for major depression. SHILAP Revista de lepidopterología. 10(1). 25–25. 3 indexed citations
8.
Fang, Ruogu, et al.. (2024). DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction. PLoS Computational Biology. 20(4). e1011351–e1011351. 1 indexed citations
9.
Lu, Yi, et al.. (2024). A semantic segmentation method to analyze retinal vascular parameters of diabetic nephropathy. Frontiers in Medicine. 11. 1494659–1494659.
10.
Liu, Peng, et al.. (2024). Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects. PLoS Computational Biology. 20(3). e1011943–e1011943. 1 indexed citations
11.
Louis‐Jacques, Adetola, et al.. (2023). Ethnic disparity in diagnosing asymptomatic bacterial vaginosis using machine learning. npj Digital Medicine. 6(1). 211–211. 16 indexed citations
12.
Huang, Hong, et al.. (2023). Texture and motion aware perception in-loop filter for AV1. Journal of Visual Communication and Image Representation. 98. 104025–104025. 3 indexed citations
13.
Sheng, Bin, Ruogu Fang, Huating Li, et al.. (2020). Domain-invariant interpretable fundus image quality assessment. Medical Image Analysis. 61. 101654–101654. 66 indexed citations
14.
Masood, Saleha, Ruogu Fang, Ping Li, et al.. (2019). Automatic Choroid Layer Segmentation from Optical Coherence Tomography Images Using Deep Learning. Scientific Reports. 9(1). 3058–3058. 77 indexed citations
15.
Liu, Peng, et al.. (2019). Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising. Medical Image Analysis. 54. 306–315. 67 indexed citations
16.
Sheng, Bin, Ping Li, Huating Li, et al.. (2018). Retinal Vessel Segmentation Using Minimum Spanning Superpixel Tree Detector. IEEE Transactions on Cybernetics. 49(7). 2707–2719. 56 indexed citations
17.
Liu, Peng & Ruogu Fang. (2017). Wide Inference Network for Image Denoising.. arXiv (Cornell University). 10 indexed citations
18.
Jiang, Fei, Huating Li, Xuhong Hou, et al.. (2016). Abdominal adipose tissues extraction using multi-scale deep neural network. Neurocomputing. 229. 23–33. 19 indexed citations
19.
Fang, Ruogu, et al.. (2015). Tissue-specific sparse deconvolution for brain CT perfusion. Computerized Medical Imaging and Graphics. 46. 64–72. 4 indexed citations
20.
Fang, Ruogu, et al.. (2013). Improving low-dose blood–brain barrier permeability quantification using sparse high-dose induced prior for Patlak model. Medical Image Analysis. 18(6). 866–880. 12 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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