Junting Zhang

1.2k citations
20 papers · 766 indexed · h-index 9
Topics
Intracranial Aneurysms: Treatment and Complications (6 papers)Domain Adaptation and Few-Shot Learning (6 papers)Advanced Neural Network Applications (5 papers)
Partner nations
ChinaUnited StatesTaiwan

In The Last Decade

Junting Zhang

19 papers receiving 751 citations

Peers

Junting Zhang
Comparison fields: 5 of 81
  • Neurology 416
  • Artificial Intelligence 240
  • Computer Vision and Pattern Recognition 192
  • Epidemiology 97
  • Molecular Biology 77
Replace Zengmin Tian with:
Zengmin Tian China
Jie Ni China
Melih Tütüncü Türkiye
Oskar Maier Germany
Yuqi Fang China
Zilong Hu China
Oliver Lange Germany
Guidong Song China
José Bernal United Kingdom
Akmal A. Younis United States
Junting Zhang relative to Zengmin Tian China Zengmin Tian's profile →
Citations per field
00.5×3.8×
Zengmin Tian · 1×
Citations per year

Countries citing papers authored by Junting Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Junting Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junting Zhang

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 5
2 6
3 6
4 5
5 196
6 10
7
Efficient Incremental Learning for Mobile Object Detection
4
8 6
9 0
10 59
11 7
12 16
13 90
14 6
15 35
16 43
17 60
18 3
19 6
20 203

About Junting Zhang

Junting Zhang is a scholar working on Neurology, Computer Vision and Pattern Recognition and Health Information Management, having authored 20 papers that have together received 766 indexed citations. Recurring topics across this work include Intracranial Aneurysms: Treatment and Complications (6 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Advanced Neural Network Applications (5 papers). The work is most often cited by research in Neurology (416 citations), Computer Vision and Pattern Recognition (192 citations) and Artificial Intelligence (240 citations). Junting Zhang has collaborated with scholars based in China, United States and Taiwan. Frequent co-authors include Chung‐Cheng Wang, Ali Liu, Bo Sun, Yuanli Zhao, Shalini Ghosh, Dawei Li, Larry Heck, Şerafettin Taşcı, C.‐C. Jay Kuo and Heming Zhang. Their work appears in journals such as Scientific Reports, Journal of neurosurgery and Sensors.

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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