Ji Eun Oh

479 total citations
29 papers, 317 citations indexed

About

Ji Eun Oh is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Ji Eun Oh has authored 29 papers receiving a total of 317 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Radiology, Nuclear Medicine and Imaging, 12 papers in Pulmonary and Respiratory Medicine and 8 papers in Biomedical Engineering. Recurrent topics in Ji Eun Oh's work include Radiomics and Machine Learning in Medical Imaging (10 papers), Digital Radiography and Breast Imaging (9 papers) and Medical Imaging Techniques and Applications (7 papers). Ji Eun Oh is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (10 papers), Digital Radiography and Breast Imaging (9 papers) and Medical Imaging Techniques and Applications (7 papers). Ji Eun Oh collaborates with scholars based in South Korea, United States and Germany. Ji Eun Oh's co-authors include Dae Kyung Sohn, Joohyung Lee, Kwang Gi Kim, Bo Yun Hur, Hee Jin Chang, Min Ju Kim, Hee Kyung Yang, Jeong‐Min Hwang, Seonhye Lee and Hong‐Jun Yoon and has published in prestigious journals such as IEEE Access, Medicine and Investigative Ophthalmology & Visual Science.

In The Last Decade

Ji Eun Oh

27 papers receiving 308 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ji Eun Oh South Korea 9 192 97 77 48 45 29 317
Yongheng Luo China 10 147 0.8× 43 0.4× 23 0.3× 62 1.3× 55 1.2× 28 364
Giovanni Irmici Italy 9 163 0.8× 57 0.6× 14 0.2× 72 1.5× 61 1.4× 24 318
Weidao Chen China 11 153 0.8× 39 0.4× 12 0.2× 72 1.5× 56 1.2× 25 254
Ajay Patel Netherlands 8 112 0.6× 44 0.5× 20 0.3× 45 0.9× 37 0.8× 13 270
Sungwon Ham South Korea 9 202 1.1× 69 0.7× 19 0.2× 63 1.3× 90 2.0× 18 385
Mohamed Elsharkawy United States 13 334 1.7× 62 0.6× 11 0.1× 48 1.0× 31 0.7× 33 549
Liyun Chang Taiwan 11 150 0.8× 17 0.2× 28 0.4× 45 0.9× 153 3.4× 45 379
Majid Assadi Iran 9 144 0.8× 34 0.4× 38 0.5× 23 0.5× 61 1.4× 27 246
Mircea-Sebastian Şerbănescu Romania 10 118 0.6× 134 1.4× 71 0.9× 41 0.9× 74 1.6× 90 422
Matteo Pepa Italy 9 155 0.8× 43 0.4× 40 0.5× 75 1.6× 109 2.4× 33 332

Countries citing papers authored by Ji Eun Oh

Since Specialization
Citations

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

Fields of papers citing papers by Ji Eun Oh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ji Eun Oh

This figure shows the co-authorship network connecting the top 25 collaborators of Ji Eun Oh. A scholar is included among the top collaborators of Ji Eun Oh 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 Ji Eun Oh. Ji Eun Oh 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.
Oh, Ji Eun, Eun Young Park, Hyae Young Kim, et al.. (2025). Prediction of Lymph Node Metastasis in Lung Cancer Using Deep Learning of Endobronchial Ultrasound Images With Size on CT and PET‐CT Findings. Respirology. 30(6). 515–522. 1 indexed citations
2.
Oh, Ji Eun, et al.. (2024). End-to-End Semi-Supervised Opportunistic Osteoporosis Screening Using Computed Tomography. Endocrinology and Metabolism. 39(3). 500–510. 4 indexed citations
3.
4.
Oh, Ji Eun, et al.. (2024). A Study on the Recognition and Treatment Rate of the Three Major Eye Diseases. The Korean Journal of Vision Science. 26(2). 129–136.
5.
Lee, Joohyung, et al.. (2019). Multi-Task Learning with a Fully Convolutional Network for Rectum and Rectal Cancer Segmentation.. arXiv (Cornell University). 1 indexed citations
6.
Oh, Ji Eun, Min Ju Kim, Joohyung Lee, et al.. (2019). Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer. Cancer Research and Treatment. 52(1). 51–59. 57 indexed citations
7.
Park, Ju Young, et al.. (2018). Factors Influencing the Infection Control Practice of Clinical Nurses based on Health Belief Model. Journal of the Korean Chemical Society. 9(3). 121–129. 6 indexed citations
8.
Oh, Ji Eun & Ju Young Park. (2018). Influencing Factors on Performance for Standard Precaution of Healthcare Workers of General Hospital for Infection Control. Journal of Digital Convergence. 16(4). 231–249. 8 indexed citations
9.
Kim, Keun Su, S. Y. Park, Hyosung Cho, et al.. (2018). A Compressed-Sensing Based Blind Deconvolution Method for Image Deblurring in Dental Cone-Beam Computed Tomography. Journal of Digital Imaging. 32(3). 478–488. 1 indexed citations
10.
Yoon, Hong‐Jun, Joohyung Lee, Ji Eun Oh, et al.. (2018). Tumor Identification in Colorectal Histology Images Using a Convolutional Neural Network. Journal of Digital Imaging. 32(1). 131–140. 46 indexed citations
11.
Oh, Ji Eun, et al.. (2018). Asymptomatic unruptured intracranial aneurysms in the older people. European Geriatric Medicine. 10(1). 119–127. 4 indexed citations
12.
Kim, W., Chankyu Park, Gun Min Kim, et al.. (2018). A blind-deblurring method based on a compressed-sensing scheme in digital breast tomosynthesis. Optics and Lasers in Engineering. 110. 228–235. 6 indexed citations
13.
Kang, Sang‐Hoon, W. Kim, Chanseok Park, et al.. (2018). A new software scheme for scatter correction based on a simple radiographic scattering model. Medical & Biological Engineering & Computing. 57(2). 489–503. 5 indexed citations
14.
Shin, Byung Chul, et al.. (2018). Association between indication for therapy by nutrition support team and nutritional status. Medicine. 97(52). e13932–e13932. 17 indexed citations
15.
Oh, Ji Eun, et al.. (2018). Clustered Microcalcification Detection in Digital Mammography for Various Breast Densities. Journal of Medical Imaging and Health Informatics. 8(5). 1103–1112. 1 indexed citations
16.
Kim, Kwang Taek, Hanna Cho, Woo‐Seok Kang, et al.. (2017). A model-based radiography restoration method based on simple scatter-degradation scheme for improving image visibility. Optics and Lasers in Engineering. 101. 60–66. 2 indexed citations
17.
Han, Sang Beom, Hee Kyung Yang, Ji Eun Oh, Kwang Gi Kim, & Jeong‐Min Hwang. (2016). Efficacy of automated computer-aided diagnosis of retinal nerve fibre layer defects in healthcare screening. British Journal of Ophthalmology. 101(3). bjophthalmol–2015. 1 indexed citations
18.
Yoon, Woong, et al.. (2016). Automatic Detection of Pectoral Muscle Region for Computer-Aided Diagnosis Using MIAS Mammograms. BioMed Research International. 2016. 1–6. 34 indexed citations
19.
Choi, Seungwon, et al.. (2010). Development of a digital panoramic X-ray imaging system of adaptive image layers for dental applications. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 652(1). 767–770. 2 indexed citations
20.
Jung, Sung‐No, Jong Won Rhie, Ho Jeong Kwon, et al.. (2010). In Vivo Cartilage Formation Using Chondrogenic-Differentiated Human Adipose-Derived Mesenchymal Stem Cells Mixed With Fibrin Glue. Journal of Craniofacial Surgery. 21(2). 468–472. 43 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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