Soochahn Lee

791 total citations
30 papers, 477 citations indexed

About

Soochahn Lee is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Ophthalmology. According to data from OpenAlex, Soochahn Lee has authored 30 papers receiving a total of 477 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 14 papers in Radiology, Nuclear Medicine and Imaging and 8 papers in Ophthalmology. Recurrent topics in Soochahn Lee's work include Retinal Imaging and Analysis (10 papers), Medical Image Segmentation Techniques (8 papers) and Advanced Vision and Imaging (6 papers). Soochahn Lee is often cited by papers focused on Retinal Imaging and Analysis (10 papers), Medical Image Segmentation Techniques (8 papers) and Advanced Vision and Imaging (6 papers). Soochahn Lee collaborates with scholars based in South Korea, Puerto Rico and Ethiopia. Soochahn Lee's co-authors include Il Dong Yun, Kyoung Mu Lee, Seung Yeon Shin, Sang Jun Park, Yong Seok Heo, Ho Yub Jung, Sang Uk Lee, Kyong Joon Lee, Kyu Hyung Park and Leonard Sunwoo and has published in prestigious journals such as PLoS ONE, Scientific Reports and IEEE Access.

In The Last Decade

Soochahn Lee

25 papers receiving 461 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Soochahn Lee South Korea 10 293 255 153 72 69 30 477
Jianyang Xie China 11 379 1.3× 130 0.5× 279 1.8× 96 1.3× 44 0.6× 31 497
Veena Mayya India 9 148 0.5× 166 0.7× 71 0.5× 22 0.3× 67 1.0× 32 374
Tati L. R. Mengko Indonesia 12 103 0.4× 147 0.6× 29 0.2× 70 1.0× 67 1.0× 64 386
T. Shanthi India 7 202 0.7× 142 0.6× 89 0.6× 21 0.3× 108 1.6× 10 425
Abouzar Eslami Germany 12 278 0.9× 154 0.6× 132 0.9× 206 2.9× 43 0.6× 46 501
Paweł Liskowski Poland 8 553 1.9× 424 1.7× 363 2.4× 34 0.5× 158 2.3× 17 742
Pierre-Henri Conze France 14 304 1.0× 277 1.1× 64 0.4× 87 1.2× 199 2.9× 47 616
Václav Uher Czechia 9 167 0.6× 199 0.8× 121 0.8× 54 0.8× 64 0.9× 22 369
Rongchang Zhao China 15 554 1.9× 367 1.4× 366 2.4× 54 0.8× 128 1.9× 41 767
Ehsan Saeedi Australia 9 137 0.5× 85 0.3× 83 0.5× 42 0.6× 121 1.8× 22 354

Countries citing papers authored by Soochahn Lee

Since Specialization
Citations

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

Fields of papers citing papers by Soochahn Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Soochahn Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Soochahn Lee. A scholar is included among the top collaborators of Soochahn Lee 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 Soochahn Lee. Soochahn Lee 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.
Lee, Jae Hyuk, Shuai An, Sungyong Baik, & Soochahn Lee. (2025). CoAPT: Context Attribute words for Prompt Tuning. Knowledge-Based Systems. 320. 113653–113653.
2.
Jung, Sung‐Hee, et al.. (2024). Cycle Consistent Generative Motion Artifact Correction in Coronary Computed Tomography Angiography. Applied Sciences. 14(5). 1859–1859. 1 indexed citations
3.
Park, Kyu Hyung, et al.. (2024). A novel vector field analysis for quantitative structure changes after macular epiretinal membrane surgery. Scientific Reports. 14(1). 8242–8242.
4.
Song, Su Jeong, et al.. (2023). A deep learning-based framework for retinal fundus image enhancement. PLoS ONE. 18(3). e0282416–e0282416. 12 indexed citations
5.
Lee, Soochahn, Seok Kim, Kyu Hyung Park, et al.. (2021). An innovative strategy for standardized, structured, and interoperable results in ophthalmic examinations. BMC Medical Informatics and Decision Making. 21(1). 9–9. 8 indexed citations
6.
Jung, Sung‐Hee, et al.. (2020). Deep Learning Cross-Phase Style Transfer for Motion Artifact Correction in Coronary Computed Tomography Angiography. IEEE Access. 8. 81849–81863. 14 indexed citations
7.
Byun, Seong Jun, Soochahn Lee, Tackeun Kim, et al.. (2020). Effects of Hypertension, Diabetes, and Smoking on Age and Sex Prediction from Retinal Fundus Images. Scientific Reports. 10(1). 4623–4623. 47 indexed citations
8.
Shin, Seung Yeon, et al.. (2020). Triplanar convolution with shared 2D kernels for 3D classification and shape retrieval. Computer Vision and Image Understanding. 193. 102901–102901. 7 indexed citations
9.
Park, Sang Jun, et al.. (2020). Multimodal Registration of Fundus Images With Fluorescein Angiography for Fine-Scale Vessel Segmentation. IEEE Access. 8. 63757–63769. 9 indexed citations
10.
Shin, Seung Yeon, Soochahn Lee, Il Dong Yun, & Kyoung Mu Lee. (2020). Topology-Aware Retinal Artery–Vein Classification via Deep Vascular Connectivity Prediction. Applied Sciences. 11(1). 320–320. 4 indexed citations
11.
Park, Sang Jun, et al.. (2019). Scale-space approximated convolutional neural networks for retinal vessel segmentation. Computer Methods and Programs in Biomedicine. 178. 237–246. 53 indexed citations
12.
Shin, Seung Yeon, Soochahn Lee, Il Dong Yun, & Kyoung Mu Lee. (2019). Deep vessel segmentation by learning graphical connectivity. Medical Image Analysis. 58. 101556–101556. 158 indexed citations
13.
Lee, Soochahn, et al.. (2015). Structured patch model for a unified automatic and interactive segmentation framework. Medical Image Analysis. 24(1). 297–312. 4 indexed citations
14.
Shin, Seung Yeon, Soochahn Lee, Il Dong Yun, et al.. (2015). A Novel Cascade Classifier for Automatic Microcalcification Detection. PLoS ONE. 10(12). e0143725–e0143725. 6 indexed citations
15.
Heo, Yong Seok, Soochahn Lee, & Ho Yub Jung. (2015). Consistent color and detail transfer from multiple source images for video and images. The Visual Computer. 32(10). 1273–1289.
16.
Jung, Ho Yub, et al.. (2015). Random tree walk toward instantaneous 3D human pose estimation. 2467–2474. 58 indexed citations
17.
Lee, Soochahn, et al.. (2013). Hierarchical MRF of globally consistent localized classifiers for 3D medical image segmentation. Pattern Recognition. 46(9). 2408–2419. 12 indexed citations
18.
Lee, Soochahn, et al.. (2011). Optimization of local shape and appearance probabilities for segmentation of knee cartilage in 3-D MR images. Computer Vision and Image Understanding. 115(12). 1710–1720. 29 indexed citations
19.
Lee, Soochahn, Il Dong Yun, & Sang Uk Lee. (2010). Robust bilayer video segmentation by adaptive propagation of global shape and local appearance. Journal of Visual Communication and Image Representation. 21(7). 665–676. 9 indexed citations
20.
Lee, Soochahn, Hackjoon Shim, Il Dong Yun, et al.. (2009). Fully automatic 3-D segmentation of knee bone compartments by iterative local branch-and-mincut on MR images from osteoarthritis initiative (OAI). 23. 3381–3384. 4 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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