Min‐Sun Jin

32 papers receiving 453 citations

Peers

Min‐Sun Jin
Comparison fields: 5 of 56
  • Health Informatics 14
  • Cancer Research 85
  • Geriatrics and Gerontology 21
  • Radiology, Nuclear Medicine and Imaging 119
  • Oncology 126
Replace Hyeyoon Chang with:
Hyeyoon Chang South Korea
Francesca Valdora Italy
Masashi Takawa Japan
Geunwon Kim United States
Daixing Hu China
Jialiang Shao China
Yoshiaki Sagara Japan
Yusufu Maimaiti China
Jurui Luo China
Antonia Márquez Spain
Min‐Sun Jin relative to Hyeyoon Chang South Korea Hyeyoon Chang's profile →
Citations per field
00.5×1.7×
Hyeyoon Chang · 1×
Citations per year

Countries citing papers authored by Min‐Sun Jin

Since Specialization
Citations

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

Fields of papers citing papers by Min‐Sun Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Min‐Sun Jin. 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 Min‐Sun Jin. The network helps show where Min‐Sun Jin may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201081
2 201657
3 201945
4 201637
5 201237
6 201534
7 201621
8 201620
9 202217
10 201815
11 201915
12 201815
13 201612
14 20218
15 20226
16 20085
17
Fine Needle Aspiration Cytology of Granular Cell Tumor in Breast -A Case Report-
20074
18 20234
19 20233
20 20193

About Min‐Sun Jin

Min‐Sun Jin is a scholar working on Oncology, Molecular Biology, Pathology and Forensic Medicine, Pulmonary and Respiratory Medicine and Epidemiology, having authored 32 papers that have together received 457 indexed citations. Recurring topics across this work include Cancer Cells and Metastasis (7 papers), Molecular Biology Techniques and Applications (4 papers), Cervical Cancer and HPV Research (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Lymphoma Diagnosis and Treatment (3 papers), Breast Cancer Treatment Studies (3 papers), AI in cancer detection (3 papers) and Medical Imaging and Pathology Studies (2 papers). The work is most often cited by research in Health Informatics (14 citations), Cancer Research (85 citations), Geriatrics and Gerontology (21 citations), Radiology, Nuclear Medicine and Imaging (119 citations) and Oncology (126 citations). Min‐Sun Jin has collaborated with scholars based in South Korea, United States and Cyprus. Frequent co-authors include Han Suk Ryu, Yul Ri Chung, Seock‐Ah Im, Hyeong‐Gon Moon, Sung Hun Kim, Byung Joo Song, Bong Joo Kang, Kyung-Hun Lee, Ahwon Lee and In Ae Park. Their work appears in journals such as Human Molecular Genetics, Tumor Biology, Acta Cytologica, Scientific Reports and British Journal of 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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