Huoling Luo

535 citations
22 papers · 305 · h-index 7

Impact in

Papers in

Huoling Luo

19 papers receiving 301 citations

Peers

Huoling Luo
Comparison fields: 5 of 55
  • Health Informatics 11
  • Nephrology 48
  • Hepatology 50
  • Computer Vision and Pattern Recognition 111
  • Surgery 118
Replace Katia Passera with:
Katia Passera Italy
Shugeng Zhang China
Rui Tang China
Jeon‐Hor Chen Taiwan
Jun Ki Min South Korea
Daniel Smutek Czechia
Kishore Gopalakrishnan United Kingdom
Cheng Zhao China
Nick Weiss Germany
Ayana Okamoto Japan
Huoling Luo relative to Katia Passera Italy Katia Passera's profile →
Citations per field
00.5×3.7×
Katia Passera · 1×
Citations per year

Countries citing papers authored by Huoling Luo

Since Specialization
Citations

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

Fields of papers citing papers by Huoling Luo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Huoling Luo, 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 Huoling Luo Line = papers co-authored together Huoling Luo links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201974
2 201972
3 200567
4 201924
5 202117
6 20238
7 20236
8 20166
9 20125
10 20105
11 20173
12 20123
13 20213
14 20243
15 20193
16 20252
17 20232
18 20241
19 20251
20 20250

About Huoling Luo

Huoling Luo is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering, Surgery, Aerospace Engineering and Molecular Biology, having authored 22 papers that have together received 305 indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (6 papers), Medical Image Segmentation Techniques (5 papers), Medical Imaging and Analysis (4 papers), Augmented Reality Applications (4 papers), Surgical Simulation and Training (4 papers), Soft Robotics and Applications (3 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Advanced Vision and Imaging (3 papers). The work is most often cited by research in Health Informatics (11 citations), Nephrology (48 citations), Hepatology (50 citations), Computer Vision and Pattern Recognition (111 citations) and Surgery (118 citations). Huoling Luo has collaborated with scholars based in China, Hong Kong and Canada. Frequent co-authors include Fucang Jia, Qingmao Hu, Chihua Fang, Qiao Zhou, Baihai Su, Wei Qin, Fan Jun, Yingfang Fan, Wen Zhu and Jian Yang. Their work appears in journals such as International Journal of Computer Assisted Radiology and Surgery, Medical Physics, World Allergy Organization Journal, IEEE Journal of Biomedical and Health Informatics and Journal of Cellular Physiology.

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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