Danni Ai

2.1k citations
154 papers · 1.4k indexed · h-index 19

Danni Ai

135 papers receiving 1.4k citations

Peers

Danni Ai
Comparison fields: 5 of 125
  • Computer Vision and Pattern Recognition 776
  • Radiology, Nuclear Medicine and Imaging 501
  • Media Technology 127
  • Neurology 86
  • Biomedical Engineering 392
Replace Jingfan Fan with:
Jingfan Fan China
Gao Yang China
Gareth Funka-Lea United States
Boštjan Likar Slovenia
Jinming Duan United Kingdom
Farida Chériet Canada
Zhijian Song China
Guanyu Yang China
Abdel Aziz Taha Germany
Colin Elkin United States
Danni Ai relative to Jingfan Fan China Jingfan Fan's profile →
Citations per field
00.5×1.5×
Jingfan Fan · 1×
Citations per year

Countries citing papers authored by Danni Ai

Since Specialization
Citations

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

Fields of papers citing papers by Danni Ai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
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13 20230
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15 20236
16 20204
17 20196
18 201913
19 201856
20
Auto-recognition of food images using SPIN feature for Food-Log system
201212

About Danni Ai

Danni Ai is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Computational Mathematics, Media Technology and Computer Graphics and Computer-Aided Design, having authored 154 papers that have together received 1.4k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (58 papers), Retinal Imaging and Analysis (26 papers), Robotics and Sensor-Based Localization (16 papers), Advanced Image and Video Retrieval Techniques (16 papers), Cerebrovascular and Carotid Artery Diseases (15 papers), Medical Imaging and Analysis (15 papers), Advanced Neural Network Applications (13 papers) and Advanced Vision and Imaging (13 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (776 citations), Radiology, Nuclear Medicine and Imaging (501 citations), Media Technology (127 citations), Neurology (86 citations) and Biomedical Engineering (392 citations). Danni Ai has collaborated with scholars based in China, Japan and United States. Frequent co-authors include Jian Yang, Yongtian Wang, Jingfan Fan, Hong Song, Yong Huang, Songyuan Tang, Mubashir Ahmad, Syed Furqan Qadri, Yitian Zhao and Xuehu Wang. Their work appears in journals such as Physics in Medicine and Biology, Computer Methods and Programs in Biomedicine, Computers in Biology and Medicine, Neurocomputing and IEEE Transactions on Circuits and Systems for Video Technology.

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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