Ling Dai

2.0k citations
10 papers · 566 indexed · 1 hit paper · h-index 6
Topics
Retinal Imaging and Analysis (5 papers)Retinal Diseases and Treatments (4 papers)Retinal and Optic Conditions (3 papers)

In The Last Decade

Ling Dai

9 papers receiving 543 citations

Hit Papers

A deep learning system for detecting diabetic retinopathy...20212026202220242021100200300

Peers

Ling Dai
Comparison fields: 5 of 66
  • Radiology, Nuclear Medicine and Imaging 417
  • Ophthalmology 277
  • Computer Vision and Pattern Recognition 189
  • Health Information Management 103
  • Artificial Intelligence 92
Replace Samiksha Pachade with:
Samiksha Pachade United States
Rubina Sarki Australia
Bruno Laÿ United States
Jahanzaib Latif China
Parham Khojasteh Australia
Joaquim de Moura Spain
A. Krishna Rao India
Neha Gour India
Kevin Noronha India
Xianglong Zeng China
Ling Dai relative to Samiksha Pachade United States Samiksha Pachade's profile →
Citations per field
00.5×
Samiksha Pachade · 1×
Citations per year

Countries citing papers authored by Ling Dai

Since Specialization
Citations

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

Fields of papers citing papers by Ling Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ling Dai

This figure shows the co-authorship network connecting the top 25 collaborators of Ling Dai. A scholar is included among the top collaborators of Ling Dai 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 Ling Dai. Ling Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
#WorkIndexed citations
1 0
2 1
3
A deep learning system for detecting diabetic retinopathy across the disease spectrumbreakdown →
319
4 1
5 66
6 19
7 23
8 125
9 8
10 4

About Ling Dai

Ling Dai is a scholar working on Ophthalmology, Energy Engineering and Power Technology and Health Information Management, having authored 10 papers that have together received 566 indexed citations. Recurring topics across this work include Retinal Imaging and Analysis (5 papers), Retinal Diseases and Treatments (4 papers) and Retinal and Optic Conditions (3 papers). The work is most often cited by research in Ophthalmology (277 citations), Health Information Management (103 citations) and Radiology, Nuclear Medicine and Imaging (417 citations). Ling Dai has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Huating Li, Weiping Jia, Qiang Wu, Xuhong Hou, Bin Sheng, Ruogu Fang, Bin Sheng, Dinggang Shen, Hongyu Kong and Xiangning Wang. Their work appears in journals such as Nature Communications, IEEE Transactions on Medical Imaging and Medical Image Analysis.

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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