Mirinae Seo

1.5k total citations
44 papers, 1.1k citations indexed

About

Mirinae Seo is a scholar working on Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine and Biomedical Engineering. According to data from OpenAlex, Mirinae Seo has authored 44 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Radiology, Nuclear Medicine and Imaging, 11 papers in Pathology and Forensic Medicine and 11 papers in Biomedical Engineering. Recurrent topics in Mirinae Seo's work include Radiomics and Machine Learning in Medical Imaging (11 papers), Breast Lesions and Carcinomas (11 papers) and MRI in cancer diagnosis (10 papers). Mirinae Seo is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (11 papers), Breast Lesions and Carcinomas (11 papers) and MRI in cancer diagnosis (10 papers). Mirinae Seo collaborates with scholars based in South Korea, Ethiopia and Puerto Rico. Mirinae Seo's co-authors include Yu‐Mee Sohn, Woo Kyung Moon, Jung Min Chang, Min Sun Bae, Nariya Cho, Won Hwa Kim, Hye Ryoung Koo, Hye Shin Ahn, Su Hyun Lee and Sung Hee Park and has published in prestigious journals such as Scientific Reports, Radiology and Frontiers in Immunology.

In The Last Decade

Mirinae Seo

43 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mirinae Seo South Korea 18 671 277 265 236 220 44 1.1k
Yu‐Mee Sohn South Korea 16 462 0.7× 133 0.5× 190 0.7× 213 0.9× 133 0.6× 49 879
Hye Mi Gweon South Korea 23 1.1k 1.6× 261 0.9× 339 1.3× 559 2.4× 196 0.9× 55 1.6k
Woo Jung Choi South Korea 22 802 1.2× 423 1.5× 236 0.9× 131 0.6× 212 1.0× 106 1.3k
In Ae Park South Korea 15 335 0.5× 300 1.1× 115 0.4× 136 0.6× 156 0.7× 28 887
Mijung Jang South Korea 19 597 0.9× 255 0.9× 244 0.9× 273 1.2× 150 0.7× 74 1.1k
Seon Hyeong Choi South Korea 21 327 0.5× 202 0.7× 99 0.4× 135 0.6× 175 0.8× 49 1.1k
Kyu Ran Cho South Korea 20 672 1.0× 267 1.0× 231 0.9× 175 0.7× 114 0.5× 75 1.1k
Hye Ryoung Koo South Korea 24 1.4k 2.1× 372 1.3× 389 1.5× 749 3.2× 284 1.3× 55 2.1k
F. Degenhardt Germany 15 576 0.9× 209 0.8× 215 0.8× 388 1.6× 141 0.6× 46 958
Dana H. Whaley United States 18 703 1.0× 171 0.6× 444 1.7× 262 1.1× 279 1.3× 43 1.2k

Countries citing papers authored by Mirinae Seo

Since Specialization
Citations

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

Fields of papers citing papers by Mirinae Seo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mirinae Seo

This figure shows the co-authorship network connecting the top 25 collaborators of Mirinae Seo. A scholar is included among the top collaborators of Mirinae Seo 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 Mirinae Seo. Mirinae Seo 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
1.
Jeon, Seung Hyuck, So‐Woon Kim, Kiyong Na, et al.. (2022). Radiomic models based on magnetic resonance imaging predict the spatial distribution of CD8+ tumor-infiltrating lymphocytes in breast cancer. Frontiers in Immunology. 13. 1080048–1080048. 5 indexed citations
2.
Koh, Jieun, Eunjung Lee, Kyunghwa Han, et al.. (2020). Diagnosis of thyroid nodules on ultrasonography by a deep convolutional neural network. Scientific Reports. 10(1). 15245–15245. 44 indexed citations
3.
Shin, So Youn, et al.. (2020). Coexisting active pulmonary tuberculosis in tuberculous spondylitis: the prevalence and the role of chest CT. Journal of Thoracic Disease. 12(4). 1635–1638.
4.
Kang, Hye Jin, Mirinae Seo, Yu‐Mee Sohn, et al.. (2019). Comparison of Diagnostic Performance of B-Mode Ultrasonography and Shear Wave Elastography in Cervical Lymph Nodes. Ultrasound Quarterly. 35(3). 290–296. 6 indexed citations
5.
Sohn, Yu‐Mee, et al.. (2018). Tumor stiffness measured by quantitative and qualitative shear wave elastography of breast cancer. British Journal of Radiology. 91(1086). 20170830–20170830. 32 indexed citations
6.
7.
Seo, Mirinae, et al.. (2017). Estimation of T2* Relaxation Time of Breast Cancer: Correlation with Clinical, Imaging and Pathological Features. Korean Journal of Radiology. 18(1). 238–238. 29 indexed citations
8.
Choi, Hye Young, Yu‐Mee Sohn, & Mirinae Seo. (2017). Comparison of 3D and 2D shear-wave elastography for differentiating benign and malignant breast masses: focus on the diagnostic performance. Clinical Radiology. 72(10). 878–886. 20 indexed citations
9.
Choi, Hee Young, et al.. (2017). Preoperative Axillary Lymph Node Evaluation in Breast Cancer. Ultrasound Quarterly. 33(1). 6–14. 47 indexed citations
10.
Yoo, Jeongin, Hye Shin Ahn, Soo Jin Kim, et al.. (2017). Evaluation of Diagnostic Performance of Screening Thyroid Ultrasonography and Imaging Findings of Screening-Detected Thyroid Cancer. Cancer Research and Treatment. 50(1). 11–18. 13 indexed citations
11.
Ahn, Hye Shin, Jong Beum Lee, Mirinae Seo, Sung Hee Park, & Byung Ihn Choi. (2017). Distinguishing benign from malignant thyroid nodules using thyroid ultrasonography: utility of adding superb microvascular imaging and elastography. La radiologia medica. 123(4). 260–270. 41 indexed citations
12.
Ahn, Hye Shin, Soo Jin Kim, Sung Hee Park, & Mirinae Seo. (2016). Radiofrequency ablation of benign thyroid nodules: evaluation of the treatment efficacy using ultrasonography. ULTRASONOGRAPHY. 35(3). 244–252. 47 indexed citations
13.
Yun, Seong Jong, Yu‐Mee Sohn, & Mirinae Seo. (2016). Risk Factors for False-Negative and False-Positive Results of Magnetic Resonance Computer-Aided Evaluation in Axillary Lymph Node Staging. Journal of Computer Assisted Tomography. 40(6). 928–936. 1 indexed citations
14.
Kim, Won Hwa, Jung Min Chang, Hye Ryoung Koo, et al.. (2016). Impact of prior mammograms on combined reading of digital mammography and digital breast tomosynthesis. Acta Radiologica. 58(2). 148–155. 10 indexed citations
15.
Bae, Min Sun, Mirinae Seo, Kwang Gi Kim, In-Ae Park, & Woo Kyung Moon. (2014). Quantitative MRI morphology of invasive breast cancer: correlation with immunohistochemical biomarkers and subtypes. Acta Radiologica. 56(3). 269–275. 52 indexed citations
16.
Chang, Jung Min, et al.. (2014). Tumour volume doubling time of molecular breast cancer subtypes assessed by serial breast ultrasound. European Radiology. 24(9). 2227–2235. 72 indexed citations
17.
Gweon, Hye Mi, Nariya Cho, Mirinae Seo, A Jung Chu, & Woo Kyung Moon. (2014). Computer-aided evaluation as an adjunct to revised BI-RADS Atlas: improvement in positive predictive value at screening breast MRI. European Radiology. 24(8). 1800–1807. 8 indexed citations
18.
Lee, Su Hyun, Jung Min Chang, Won Hwa Kim, et al.. (2014). Added Value of Shear-Wave Elastography for Evaluation of Breast Masses Detected with Screening US Imaging. Radiology. 273(1). 61–69. 97 indexed citations
19.
20.
Bae, Min Sun, Woo Kyung Moon, Jung Min Chang, et al.. (2013). Breast Cancer Detected with Screening US: Reasons for Nondetection at Mammography. Radiology. 270(2). 369–377. 127 indexed citations

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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