Tae Eun Choe

447 citations
17 papers · 199 · h-index 8

Impact in

Papers in

Tae Eun Choe

17 papers receiving 187 citations

Peers

Tae Eun Choe
Comparison fields: 5 of 33
  • Computer Vision and Pattern Recognition 163
  • Ophthalmology 25
  • Radiology, Nuclear Medicine and Imaging 48
  • Artificial Intelligence 62
  • Aerospace Engineering 34
Replace Peilun Shi with:
Peilun Shi Hong Kong
Pengshuai Yin China
Jian Shi China
Yifan Zhou China
Tingyu Wang China
Sarangi D. Dissanayake Australia
Hsiao-Yu Fish Tung United States
Shengjia Chen China
Miaomiao Zhang United States
Tae Eun Choe relative to Peilun Shi Hong Kong Peilun Shi's profile →
Citations per field
00.5×4.9×
Peilun Shi · 1×
Citations per year

Countries citing papers authored by Tae Eun Choe

Since Specialization
Citations

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

Fields of papers citing papers by Tae Eun Choe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tae Eun Choe. 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 Tae Eun Choe. The network helps show where Tae Eun Choe may publish in the future.

Co-authors

The 24 scholars most cited alongside Tae Eun Choe, 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 Tae Eun Choe Line = papers co-authored together Tae Eun Choe links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1 201469
2 200627
3 200516
4 200712
5 202112
6 20069
7 20108
8 20067
9 20117
10 20137
11 20116
12 20115
13 20204
14 20074
15 20083
16 20232
17 20141

About Tae Eun Choe

Tae Eun Choe is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Ophthalmology, Artificial Intelligence and Computer Networks and Communications, having authored 17 papers that have together received 199 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (6 papers), Advanced Vision and Imaging (6 papers), Retinal Imaging and Analysis (5 papers), Video Surveillance and Tracking Methods (4 papers), Glaucoma and retinal disorders (4 papers), Multimodal Machine Learning Applications (2 papers), Video Analysis and Summarization (2 papers) and Medical Image Segmentation Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (163 citations), Ophthalmology (25 citations), Radiology, Nuclear Medicine and Imaging (48 citations), Artificial Intelligence (62 citations) and Aerospace Engineering (34 citations). Tae Eun Choe has collaborated with scholars based in United States and China. Frequent co-authors include Mun Wai Lee, I. Cohen, Kewei Tu, Meng Meng, Song‐Chun Zhu, Gérard Medioni, Niels Haering, Srinivas R. Sadda, Alexander C. Walsh and Isaac Cohen. Their work appears in journals such as Medical Image Analysis, IEEE Multimedia, Lecture notes in computer science, Proceedings - International Conference on Pattern Recognition and arXiv (Cornell University).

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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