Dar‐Ren Chen

7.2k citations
207 papers · 5.7k · h-index 41

Impact in

Papers in

Dar‐Ren Chen

194 papers receiving 5.5k citations

Peers

Dar‐Ren Chen
Comparison fields: 5 of 170
  • Cancer Research 748
  • Radiology, Nuclear Medicine and Imaging 1.1k
  • Computer Vision and Pattern Recognition 982
  • Artificial Intelligence 1.5k
  • Pathology and Forensic Medicine 566
Replace Jianwen Chen with:
Jianwen Chen China
Michael Hoffmeister Germany
Weiling Zhao United States
Jianhua Huang China
Habtom W. Ressom United States
He Wang China
Lu Liang China
Dong‐Young Noh South Korea
Shao Li China
Ming Tao China
Dar‐Ren Chen relative to Jianwen Chen China Jianwen Chen's profile →
Citations per field
00.5×9.5×
Jianwen Chen · 1×
Citations per year

Countries citing papers authored by Dar‐Ren Chen

Since Specialization
Citations

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

Fields of papers citing papers by Dar‐Ren Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dar‐Ren Chen. 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 Dar‐Ren Chen. The network helps show where Dar‐Ren Chen may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 207 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010264
2 2002232
3 2009227
4 2002172
5 2004168
6 2005166
7 2000154
8 2020134
9 2003117
10 2005116
11 2005106
12 2003102
13 200196
14 200792
15 200387
16 201471
17 201170
18
Capsaicin-induced apoptosis in human breast cancer MCF-7 cells through caspase-independent pathway.
200970
19 201368
20 201667

About Dar‐Ren Chen

Dar‐Ren Chen is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine, Cancer Research and Molecular Biology, having authored 207 papers that have together received 5.7k indexed citations. Recurring topics across this work include AI in cancer detection (54 papers), Breast Lesions and Carcinomas (39 papers), Breast Cancer Treatment Studies (31 papers), Radiomics and Machine Learning in Medical Imaging (23 papers), Ultrasound Imaging and Elastography (18 papers), Medical Image Segmentation Techniques (15 papers), Breast Implant and Reconstruction (14 papers) and Estrogen and related hormone effects (9 papers). The work is most often cited by research in Cancer Research (748 citations), Radiology, Nuclear Medicine and Imaging (1.1k citations), Computer Vision and Pattern Recognition (982 citations), Artificial Intelligence (1.5k citations) and Pathology and Forensic Medicine (566 citations). Dar‐Ren Chen has collaborated with scholars based in Taiwan, South Korea and China. Frequent co-authors include Ruey‐Feng Chang, Yu-Len Huang, Woo Kyung Moon, Wen-Jie Wu, Shou‐Jen Kuo, Shou-Jen Kuo, Shou‐Tung Chen, Wen-Jia Kuo, Hung‐Wen Lai and Woo Kyung Moon. Their work appears in journals such as Ultrasound in Medicine & Biology, PLoS ONE, World Journal of Surgical Oncology, Breast Cancer Research and Treatment and Breast Cancer.

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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