Donghoon Yu

470 citations
17 papers · 295 · h-index 5

Impact in

Papers in

Donghoon Yu

16 papers receiving 291 citations

Peers

Donghoon Yu
Comparison fields: 5 of 65
  • Health Informatics 11
  • Pulmonary and Respiratory Medicine 154
  • Radiology, Nuclear Medicine and Imaging 107
  • Dermatology 26
  • Computer Vision and Pattern Recognition 41
Replace Wei-Lin Chen with:
Wei-Lin Chen Taiwan
Xiaojing Kang China
Deborah Thompson United States
Alexey Pryalukhin Germany
Boram Song South Korea
Ridhi Arora India
Arne Juette United Kingdom
Jinman Kim Australia
Zhaoan Su China
Ran Gu China
Donghoon Yu relative to Wei-Lin Chen Taiwan Wei-Lin Chen's profile →
Citations per field
00.5×10×13×
Wei-Lin Chen · 1×
Citations per year

Countries citing papers authored by Donghoon Yu

Since Specialization
Citations

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

Fields of papers citing papers by Donghoon Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

17 of 17 papers shown
#Work
1 202183
2 201875
3 201353
4 202234
5 202225
6 20214
7 20204
8 20243
9 20163
10 20242
11 20112
12 20222
13 20032
14 20251
15 20021
16 20051
17 20250

About Donghoon Yu

Donghoon Yu is a scholar working on Pulmonary and Respiratory Medicine, Computer Vision and Pattern Recognition, Oncology, Radiology, Nuclear Medicine and Imaging and Molecular Biology, having authored 17 papers that have together received 295 indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (7 papers), Cancer-related Molecular Pathways (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Advanced Breast Cancer Therapies (3 papers), Medical Image Segmentation Techniques (3 papers), Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (2 papers), Image and Object Detection Techniques (2 papers) and Advanced Data Compression Techniques (2 papers). The work is most often cited by research in Health Informatics (11 citations), Pulmonary and Respiratory Medicine (154 citations), Radiology, Nuclear Medicine and Imaging (107 citations), Dermatology (26 citations) and Computer Vision and Pattern Recognition (41 citations). Donghoon Yu has collaborated with scholars based in South Korea, Netherlands and China. Frequent co-authors include Jaeyoun Yi, Joon Beom Seo, Jihye Yun, Namkug Kim, Jooae Choe, June‐Goo Lee, Hee-Jun Park, Sang Min Lee, Minho Lee and Hye Jeon Hwang. Their work appears in journals such as Cancer Research, Radiology, European Journal of Radiology, Journal of Clinical Oncology and Scientific Reports.

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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