Seong Tae Kim

807 total citations
54 papers, 424 citations indexed

About

Seong Tae Kim is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Seong Tae Kim has authored 54 papers receiving a total of 424 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 27 papers in Computer Vision and Pattern Recognition and 11 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Seong Tae Kim's work include AI in cancer detection (11 papers), Face recognition and analysis (8 papers) and Advanced Neural Network Applications (8 papers). Seong Tae Kim is often cited by papers focused on AI in cancer detection (11 papers), Face recognition and analysis (8 papers) and Advanced Neural Network Applications (8 papers). Seong Tae Kim collaborates with scholars based in South Korea, Germany and United States. Seong Tae Kim's co-authors include Yong Man Ro, Dae Hoe Kim, Hong Joo Lee, Kun Chang Lee, Hakmin Lee, Seung Uk Choi, Nassir Navab, Nhu Van Nguyen, Jung Min Chang and JW Lee and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Sensors.

In The Last Decade

Seong Tae Kim

48 papers receiving 405 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Seong Tae Kim South Korea 12 187 177 103 77 57 54 424
Jean-Christophe Burie France 13 300 1.6× 102 0.6× 119 1.2× 51 0.7× 90 1.6× 44 515
Omar Hisham Alsadoon Iraq 11 129 0.7× 149 0.8× 83 0.8× 42 0.5× 26 0.5× 37 375
Fanyang Meng China 13 327 1.7× 297 1.7× 71 0.7× 76 1.0× 58 1.0× 57 657
Nicola Falcionelli Italy 11 155 0.8× 162 0.9× 58 0.6× 53 0.7× 37 0.6× 24 372
Woong-Kee Loh South Korea 12 110 0.6× 222 1.3× 96 0.9× 184 2.4× 15 0.3× 46 548
Sheeraz Akram Pakistan 17 176 0.9× 222 1.3× 237 2.3× 84 1.1× 140 2.5× 52 635
Awais Mahmood Saudi Arabia 17 185 1.0× 398 2.2× 109 1.1× 173 2.2× 13 0.2× 45 711
Akshay Vashist United States 6 263 1.4× 323 1.8× 36 0.3× 48 0.6× 8 0.1× 14 549
Asghar Ali Shah Pakistan 11 48 0.3× 93 0.5× 93 0.9× 31 0.4× 50 0.9× 47 403
Chaoyi Pang China 15 300 1.6× 268 1.5× 27 0.3× 129 1.7× 21 0.4× 61 647

Countries citing papers authored by Seong Tae Kim

Since Specialization
Citations

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

Fields of papers citing papers by Seong Tae Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seong Tae Kim. 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 Seong Tae Kim. The network helps show where Seong Tae Kim may publish in the future.

Co-authorship network of co-authors of Seong Tae Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Seong Tae Kim. A scholar is included among the top collaborators of Seong Tae Kim 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 Seong Tae Kim. Seong Tae Kim 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.
Nguyen, T. D., et al.. (2025). Unified link prediction modeling for enhanced knowledge graph completion task. Expert Systems with Applications. 279. 127356–127356.
2.
Kim, Seong Tae, et al.. (2025). Automated Radiology Report Labeling in Chest X-Ray Pathologies: Development and Evaluation of a Large Language Model Framework. JMIR Medical Informatics. 13. e68618–e68618. 2 indexed citations
3.
4.
Ko, Eunju, et al.. (2024). Changes in Pupil Size According to the Color of Cosmetic Packaging: Using Eye-Tracking Techniques. Applied Sciences. 15(1). 73–73. 1 indexed citations
5.
Kim, Hyung‐Il, et al.. (2023). Exploiting recollection effects for memory-based video object segmentation. Image and Vision Computing. 140. 104866–104866. 1 indexed citations
6.
Nguyen, Minh N. H., et al.. (2023). OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning. Neural Networks. 170. 635–649. 9 indexed citations
7.
Kim, Seong Tae, et al.. (2023). Time Series Anomaly Detection Using Transformer-Based GAN With Two-Step Masking. IEEE Access. 11. 74035–74047. 11 indexed citations
8.
Kim, Seong Tae, Magdalini Paschali, Egon Burian, et al.. (2023). Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT Scans. IEEE Access. 11. 77596–77607. 2 indexed citations
9.
Razzaq, Muhammad Asif, Jamil Hussain, Jaehun Bang, et al.. (2023). A Hybrid Multimodal Emotion Recognition Framework for UX Evaluation Using Generalized Mixture Functions. Sensors. 23(9). 4373–4373. 15 indexed citations
10.
Gomes, Douglas Pinto Sampaio, et al.. (2023). Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks. IEEE Access. 11. 35097–35110. 9 indexed citations
11.
Navab, Nassir, et al.. (2022). Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning. 2022 26th International Conference on Pattern Recognition (ICPR). 2063–2069. 7 indexed citations
12.
13.
Lee, Kun Chang, et al.. (2020). Exploring CEO Messages in Sustainability Management Reports: Applying Sentiment Mining and Sustainability Balanced Scorecard Methods. Sustainability. 12(2). 590–590. 20 indexed citations
14.
Kim, Jung Uk, Seong Tae Kim, Hong Joo Lee, Sangmin Lee, & Yong Man Ro. (2020). CUA Loss: Class Uncertainty-Aware Gradient Modulation for Robust Object Detection. IEEE Transactions on Circuits and Systems for Video Technology. 31(9). 3529–3543. 13 indexed citations
15.
Lee, Hong Joo, et al.. (2019). Lightweight and Effective Facial Landmark Detection using Adversarial Learning with Face Geometric Map Generative Network. IEEE Transactions on Circuits and Systems for Video Technology. 30(3). 771–780. 18 indexed citations
16.
Kim, Seong Tae, Jae‐Hyeok Lee, & Yong Man Ro. (2019). Visual evidence for interpreting diagnostic decision of deep neural network in computer-aided diagnosis. 1. 19–19. 4 indexed citations
17.
Kim, Seong Tae & Yong Man Ro. (2018). Attended Relation Feature Representation of Facial Dynamics for Facial Authentication. IEEE Transactions on Information Forensics and Security. 14(7). 1768–1778. 16 indexed citations
18.
Kim, Dae Hoe, Seong Tae Kim, Jung Min Chang, & Yong Man Ro. (2017). Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis. Physics in Medicine and Biology. 62(3). 1009–1031. 19 indexed citations
19.
Baddar, Wissam J., et al.. (2016). A deep facial landmarks detection with facial contour and facial components constraint. 3209–3213. 9 indexed citations
20.
Kim, Seong Tae, Dae Hoe Kim, & Yong Man Ro. (2014). Breast mass detection using slice conspicuity in 3D reconstructed digital breast volumes. Physics in Medicine and Biology. 59(17). 5003–5023. 10 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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