Le Ding

542 citations
19 papers · 258 · 1 hit paper · h-index 6

Impact in

Papers in

Le Ding

13 papers receiving 253 citations

Le Ding's Hit Papers

Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation 2023 · 142 citations
1420+1+2Years since publication4080120

Peers

Le Ding
Comparison fields: 5 of 76
  • Neurology 47
  • Computer Vision and Pattern Recognition 101
  • Health Informatics 6
  • Radiology, Nuclear Medicine and Imaging 93
  • Artificial Intelligence 68
Replace Abin Jose with:
Abin Jose Germany
Junxuan Yu China
Zhaoshuo Diao China
Rushi Jiao China
Haozhe Chi China
Xinrui Zhou China
Ashish Semwal India
Xindi Hu China
Chiun-Li Chin Taiwan
Nils Gessert Germany
Le Ding relative to Abin Jose Germany Abin Jose's profile →
Citations per field
00.5×1.5×1.9×
Abin Jose · 1×
Citations per year

Countries citing papers authored by Le Ding

Since Specialization
Citations

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

Fields of papers citing papers by Le Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

19 of 19 papers shown
#Work
1
Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation
Hit paper breakdown →
2023142
2 202269
3 202213
4 20037
5
Effects of Drought Stress on Photosynthesis and Water Status of Rice Leaves
20146
6 20085
7 20235
8 20233
9 20062
10 20242
11 20252
12 20091
13 20091
14 20190
15 20250
16 20240
17 20220
18 20040
19 20070

About Le Ding

Le Ding is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications, Psychiatry and Mental health, Physiology and Computer Vision and Pattern Recognition, having authored 19 papers that have together received 258 indexed citations. Recurring topics across this work include Advanced Wireless Communication Techniques (6 papers), Wireless Communication Networks Research (4 papers), Epilepsy research and treatment (3 papers), Advanced MIMO Systems Optimization (3 papers), Metabolism and Genetic Disorders (2 papers), Medical Image Segmentation Techniques (2 papers), Advanced Neural Network Applications (2 papers) and Diet and metabolism studies (2 papers). The work is most often cited by research in Neurology (47 citations), Computer Vision and Pattern Recognition (101 citations), Health Informatics (6 citations), Radiology, Nuclear Medicine and Imaging (93 citations) and Artificial Intelligence (68 citations). Le Ding has collaborated with scholars based in China, Bangladesh and United States. Frequent co-authors include Yichi Zhang, Jicong Zhang, Cheng Jin, Rushi Jiao, Bingsen Xue, Rong Cai, Ke Deng, Qinye Yin, Zheng Zhao and Xiaopeng Lu. Their work appears in journals such as Frontiers in Neurology, Computers in Biology and Medicine, Neuroscience, Materials Today Bio and Epilepsia Open.

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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