Jee Seok Yoon

532 total citations
15 papers, 284 citations indexed

About

Jee Seok Yoon is a scholar working on Epidemiology, Hepatology and Surgery. According to data from OpenAlex, Jee Seok Yoon has authored 15 papers receiving a total of 284 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Epidemiology, 7 papers in Hepatology and 5 papers in Surgery. Recurrent topics in Jee Seok Yoon's work include Liver Disease Diagnosis and Treatment (8 papers), Liver Disease and Transplantation (7 papers) and Organ Transplantation Techniques and Outcomes (4 papers). Jee Seok Yoon is often cited by papers focused on Liver Disease Diagnosis and Treatment (8 papers), Liver Disease and Transplantation (7 papers) and Organ Transplantation Techniques and Outcomes (4 papers). Jee Seok Yoon collaborates with scholars based in South Korea, United States and Japan. Jee Seok Yoon's co-authors include Heung‐Il Suk, Seung Soo Lee, Yu Sub Sung, Wonjun Ko, Yedaun Lee, Bo-Kyeong Kang, Ho Sung Kim, Yura Ahn, Jung Hee Son and Kang Mo Kim and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, NeuroImage and Proceedings of the IEEE.

In The Last Decade

Jee Seok Yoon

15 papers receiving 278 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jee Seok Yoon South Korea	11	108	90	75	53	53	15	284
Paul Hérent France	5	187 1.7×	51 0.6×	36 0.5×	22 0.4×	108 2.0×	6	316
Takahiro Nakao Japan	10	219 2.0×	14 0.2×	40 0.5×	112 2.1×	107 2.0×	34	460
Christine Cavaro‐Ménard France	11	79 0.7×	66 0.7×	100 1.3×	14 0.3×	16 0.3×	28	286
Fayzan Chaudhry United States	8	35 0.3×	15 0.2×	28 0.4×	27 0.5×	31 0.6×	10	280
Gang Guo China	7	83 0.8×	11 0.1×	22 0.3×	31 0.6×	110 2.1×	14	361
Georg Stamm Germany	8	142 1.3×	45 0.5×	29 0.4×	76 1.4×	13 0.2×	19	376
Kristina T. Johnson United States	11	76 0.7×	13 0.1×	33 0.4×	53 1.0×	19 0.4×	25	286
Harry T. Friel United States	8	90 0.8×	6 0.1×	35 0.5×	22 0.4×	18 0.3×	13	288
Daniel McCarthy United States	8	122 1.1×	10 0.1×	96 1.3×	10 0.2×	21 0.4×	18	335
Alexandre Bône France	7	54 0.5×	7 0.1×	17 0.2×	11 0.2×	45 0.8×	17	196

Countries citing papers authored by Jee Seok Yoon

Since Specialization

Citations

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

Fields of papers citing papers by Jee Seok Yoon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jee Seok Yoon. 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 Jee Seok Yoon. The network helps show where Jee Seok Yoon may publish in the future.

Co-authorship network of co-authors of Jee Seok Yoon

This figure shows the co-authorship network connecting the top 25 collaborators of Jee Seok Yoon. A scholar is included among the top collaborators of Jee Seok Yoon 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 Jee Seok Yoon. Jee Seok Yoon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

Yoon, Jee Seok, et al.. (2024). Domain Generalization for Medical Image Analysis: A Review. Proceedings of the IEEE. 112(10). 1583–1609. 15 indexed citations

Yoon, Jee Seok, et al.. (2023). Estimating explainable Alzheimer’s disease likelihood map via clinically-guided prototype learning. NeuroImage. 273. 120073–120073. 18 indexed citations

Lee, Seung Soo, Hyo Jung Park, Yu Sub Sung, et al.. (2023). The effect of hepatic steatosis on liver volume determined by proton density fat fraction and deep learning–measured liver volume. European Radiology. 33(9). 5924–5932. 2 indexed citations

Lee, Seung Soo, et al.. (2022). Accuracy and Efficiency of Right-Lobe Graft Weight Estimation Using Deep-Learning-Assisted CT Volumetry for Living-Donor Liver Transplantation. Diagnostics. 12(3). 590–590. 12 indexed citations

Lee, Seung Soo, So Yeon Kim, Young‐Suk Lim, et al.. (2022). Prediction of Decompensation and Death in Advanced Chronic Liver Disease Using Deep Learning Analysis of Gadoxetic Acid-Enhanced MRI. Korean Journal of Radiology. 23(12). 1269–1269. 3 indexed citations

Yoon, Jee Seok, et al.. (2022). Learn-Explain-Reinforce: Counterfactual Reasoning and its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(4). 4843–4857. 19 indexed citations

Park, Hyo Jung, Jee Seok Yoon, Seung Soo Lee, et al.. (2022). Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI. Korean Journal of Radiology. 23(7). 720–720. 13 indexed citations

Ko, Wonjun, Eunjin Jeon, Jee Seok Yoon, & Heung‐Il Suk. (2022). Semi-supervised generative and discriminative adversarial learning for motor imagery-based brain–computer interface. Scientific Reports. 12(1). 4587–4587. 8 indexed citations

Kwon, Ji Hye, Seung Soo Lee, Jee Seok Yoon, et al.. (2021). Liver-to-Spleen Volume Ratio Automatically Measured on CT Predicts Decompensation in Patients with B Viral Compensated Cirrhosis. Korean Journal of Radiology. 22(12). 1985–1985. 15 indexed citations

10.

Yoon, Jee Seok, et al.. (2021). A Plug-in Method for Representation Factorization in Connectionist Models. IEEE Transactions on Neural Networks and Learning Systems. 33(8). 3792–3803. 1 indexed citations

11.

Kim, Dong Wook, Jiyeon Ha, Seung Soo Lee, et al.. (2021). Population-based and Personalized Reference Intervals for Liver and Spleen Volumes in Healthy Individuals and Those with Viral Hepatitis. Radiology. 301(2). 339–347. 15 indexed citations

12.

Ahn, Yura, Jee Seok Yoon, Seung Soo Lee, et al.. (2020). Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images. Korean Journal of Radiology. 21(8). 987–987. 48 indexed citations

13.

Lee, Chul‐min, Seung Soo Lee, Won‐Mook Choi, et al.. (2020). An index based on deep learning–measured spleen volume on CT for the assessment of high-risk varix in B-viral compensated cirrhosis. European Radiology. 31(5). 3355–3365. 25 indexed citations

14.

Yoon, Jee Seok, et al.. (2019). Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection. Neural Networks. 115. 1–10. 61 indexed citations

15.

Ko, Wonjun, et al.. (2018). Deep recurrent spatio-temporal neural network for motor imagery based BCI. 1–3. 29 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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