Ruogu Fang

3.9k citations
71 papers · 1.6k indexed · 1 hit paper · h-index 18

Ruogu Fang

62 papers receiving 1.5k citations

Hit Papers

A Comprehensive Survey of Foundation Models in Medicine162025202651015

Peers

Ruogu Fang
Comparison fields: 5 of 133
  • Health Informatics 49
  • Ophthalmology 307
  • Health Information Management 136
  • Computer Vision and Pattern Recognition 590
  • Radiology, Nuclear Medicine and Imaging 662
Replace Joel E.W. Koh with:
Joel E.W. Koh Singapore
Bin Sheng China
Muhammad Arsalan South Korea
Fahmi Khalifa United States
Beiji Zou China
Marcos Ortega Spain
Anas Bilal China
Hossam Magdy Balaha Egypt
Mathieu Lamard France
Dwarikanath Mahapatra Switzerland
Ruogu Fang relative to Joel E.W. Koh Singapore Joel E.W. Koh's profile →
Citations per field
00.5×1.5×2.0×
Joel E.W. Koh · 1×
Citations per year

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

The 25 scholars most cited alongside Ruogu Fang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ruogu Fang Line = papers co-authored together Ruogu Fang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3
A Comprehensive Survey of Foundation Models in Medicinebreakdown →
202516
4 20250
5 20250
6 20243
7 20241
8 20240
9 202412
10 202316
11 20233
12 20228
13 20228
14 202097
15 201977
16 201856
17
Wide Inference Network for Image Denoising.
201710
18 201619
19 20154
20 20126

About Ruogu Fang

Ruogu Fang is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Biophysics, having authored 71 papers that have together received 1.6k indexed citations. Recurring topics across this work include Retinal Imaging and Analysis (13 papers), Advanced MRI Techniques and Applications (12 papers), Medical Imaging Techniques and Applications (11 papers), Image and Signal Denoising Methods (8 papers), Advanced Neuroimaging Techniques and Applications (6 papers), Cell Image Analysis Techniques (5 papers), Advanced Neural Network Applications (5 papers) and Advanced Image Processing Techniques (5 papers). The work is most often cited by research in Health Informatics (49 citations), Ophthalmology (307 citations) and Health Information Management (136 citations). Ruogu Fang has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Tsuhan Chen, Skylar E. Stolte, Pina C. Sanelli, Kevin Tang, Noah Snavely, Huating Li, Weiping Jia, Qiang Wu, Bin Sheng and S. S. Iyengar. Their work appears in journals such as SHILAP Revista de lepidopterología, NeuroImage and Scientific Reports.

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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