Taejoon Eo

898 total citations · 1 hit paper
18 papers, 564 citations indexed

About

Taejoon Eo is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Taejoon Eo has authored 18 papers receiving a total of 564 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Radiology, Nuclear Medicine and Imaging, 3 papers in Artificial Intelligence and 3 papers in Biomedical Engineering. Recurrent topics in Taejoon Eo's work include Advanced MRI Techniques and Applications (8 papers), Medical Imaging Techniques and Applications (6 papers) and Advanced Neuroimaging Techniques and Applications (6 papers). Taejoon Eo is often cited by papers focused on Advanced MRI Techniques and Applications (8 papers), Medical Imaging Techniques and Applications (6 papers) and Advanced Neuroimaging Techniques and Applications (6 papers). Taejoon Eo collaborates with scholars based in South Korea, United States and Russia. Taejoon Eo's co-authors include Dosik Hwang, Yohan Jun, Taeseong Kim, Ho‐Joon Lee, Jinseong Jang, Hyungseob Shin, In‐Yong Park, Sewon Kim, Sung Soo Ahn and Tae‐Seong Kim and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Magnetic Resonance in Medicine.

In The Last Decade

Taejoon Eo

17 papers receiving 558 citations

Hit Papers

KIKI‐net: cross‐domain convolutional neural networks for ... 2018 2026 2020 2023 2018 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images
    2018 304 citations Taejoon Eo, Yohan Jun et al. Magnetic Resonance in Medicine profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Taejoon Eo South Korea 10 428 135 90 82 62 18 564
Yohan Jun South Korea 10 459 1.1× 144 1.1× 81 0.9× 83 1.0× 35 0.6× 21 582
Jinseong Jang South Korea 8 346 0.8× 115 0.9× 117 1.3× 58 0.7× 53 0.9× 17 495
Shun Zhu China 4 449 1.0× 156 1.2× 101 1.1× 116 1.4× 22 0.4× 16 586
Taohui Xiao China 7 321 0.8× 94 0.7× 76 0.8× 57 0.7× 36 0.6× 15 383
Thanh Nguyen-Duc Australia 5 333 0.8× 138 1.0× 157 1.7× 129 1.6× 56 0.9× 11 484
Christian F. Baumgartner Germany 13 327 0.8× 129 1.0× 130 1.4× 39 0.5× 143 2.3× 30 696
Salman Ul Hassan Dar Türkiye 8 276 0.6× 86 0.6× 124 1.4× 27 0.3× 94 1.5× 16 452
Ghislain Vaillant United Kingdom 6 256 0.6× 67 0.5× 55 0.6× 23 0.3× 26 0.4× 7 339
Mahmut Yurt United States 6 296 0.7× 96 0.7× 202 2.2× 27 0.3× 112 1.8× 17 497
Niccolò Fuin United Kingdom 10 454 1.1× 99 0.7× 59 0.7× 20 0.2× 13 0.2× 17 526

Countries citing papers authored by Taejoon Eo

Since Specialization
Citations

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

Fields of papers citing papers by Taejoon Eo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Taejoon Eo

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

All Works

18 of 18 papers shown
1.
Lee, Yong‐Moon, Taejoon Eo, Hee Jung An, et al.. (2025). Multimodal AI model for preoperative prediction of axillary lymph node metastasis in breast cancer using whole slide images. npj Precision Oncology. 9(1). 131–131. 1 indexed citations
2.
3.
Hwang, Dosik, et al.. (2025). Ensemble and low-frequency mixing with diffusion models for accelerated MRI reconstruction. Medical Image Analysis. 101. 103477–103477. 1 indexed citations
4.
Lee, Hye Sun, et al.. (2024). Deep Learning-Based Joint Effusion Classification in Adult Knee Radiographs: A Multi-Center Prospective Study. Diagnostics. 14(17). 1900–1900. 1 indexed citations
5.
Shin, Hyungseob, Ji Eun Park, Yohan Jun, et al.. (2023). Deep learning referral suggestion and tumour discrimination using explainable artificial intelligence applied to multiparametric MRI. European Radiology. 33(8). 5859–5870. 10 indexed citations
6.
Eo, Taejoon, et al.. (2023). Weakly supervised deep learning for diagnosis of multiple vertebral compression fractures in CT. European Radiology. 34(6). 3750–3760. 7 indexed citations
7.
8.
Eo, Taejoon, et al.. (2022). Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering. Diagnostics. 12(8). 1858–1858. 15 indexed citations
9.
Jun, Yohan, Hyungseob Shin, Taejoon Eo, & Dosik Hwang. (2021). Joint Deep Model-based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI. 5266–5275. 42 indexed citations
10.
Jun, Yohan, Hyungseob Shin, Taejoon Eo, Taeseong Kim, & Dosik Hwang. (2021). Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method. Medical Image Analysis. 70. 102017–102017. 28 indexed citations
11.
Park, In‐Yong, et al.. (2021). Relevance-CAM: Your Model Already Knows Where to Look. 14939–14948. 43 indexed citations
12.
Shin, Hyungseob, et al.. (2020). The Latest Trends in Attention Mechanisms and Their Application in Medical Imaging. SHILAP Revista de lepidopterología. 81(6). 1305–1305. 4 indexed citations
13.
Eo, Taejoon, et al.. (2020). Accelerating Cartesian MRI by domain-transform manifold learning in phase-encoding direction. Medical Image Analysis. 63. 101689–101689. 30 indexed citations
14.
Jun, Yohan, Taejoon Eo, Hyungseob Shin, et al.. (2019). Parallel imaging in time‐of‐flight magnetic resonance angiography using deep multistream convolutional neural networks. Magnetic Resonance in Medicine. 81(6). 3840–3853. 19 indexed citations
15.
Jun, Yohan, Taejoon Eo, Tae‐Seong Kim, et al.. (2018). Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors. Scientific Reports. 8(1). 9450–9450. 34 indexed citations
16.
17.
Eo, Taejoon, Yohan Jun, Taeseong Kim, et al.. (2018). KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images. Magnetic Resonance in Medicine. 80(5). 2188–2201. 304 indexed citations breakdown →
18.
Eo, Taejoon, Taeseong Kim, Yohan Jun, et al.. (2016). High‐SNR multiple T2(*)‐contrast magnetic resonance imaging using a robust denoising method based on tissue characteristics. Journal of Magnetic Resonance Imaging. 45(6). 1835–1845. 1 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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