Boah Kim

775 total citations · 1 hit paper
13 papers, 273 citations indexed

About

Boah Kim is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Boah Kim has authored 13 papers receiving a total of 273 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 8 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Artificial Intelligence. Recurrent topics in Boah Kim's work include Medical Image Segmentation Techniques (6 papers), Advanced Neural Network Applications (4 papers) and Brain Tumor Detection and Classification (3 papers). Boah Kim is often cited by papers focused on Medical Image Segmentation Techniques (6 papers), Advanced Neural Network Applications (4 papers) and Brain Tumor Detection and Classification (3 papers). Boah Kim collaborates with scholars based in South Korea, United States and Australia. Boah Kim's co-authors include Jong Chul Ye, Seong Ho Park, Dong Hwan Kim, June‐Goo Lee, Sang Joon Park, Ronald M. Summers, Jae-Won Kim, Min-Hyeon Park, Soon-Beom Hong and Min‐Sup Shin and has published in prestigious journals such as Radiology, IEEE Transactions on Image Processing and IEEE Transactions on Medical Imaging.

In The Last Decade

Boah Kim

13 papers receiving 265 citations

Hit Papers

CycleMorph: Cycle consistent unsupervised deformable imag... 2021 2026 2022 2024 2021 50 100 150
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. CycleMorph: Cycle consistent unsupervised deformable image registration
    2021 175 citations Boah Kim, Dong Hwan Kim et al. Medical Image Analysis profile →

Peers

Boah Kim
Dinggang Shen United States
Xiuchao Sui Singapore
Tongtong Che China
Sahar Ahmad United States
Dinggang Shen United States
S. Prima France
S. J. Pawan India
C. M. Sujatha India
Hai Shu United States
Rui Hui China
Dinggang Shen United States
Boah Kim
Citations per year, relative to Boah Kim Boah Kim (= 1×) peers Dinggang Shen

Countries citing papers authored by Boah Kim

Since Specialization
Citations

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

Fields of papers citing papers by Boah Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Boah Kim

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

All Works

13 of 13 papers shown
1.
Zhuang, Yan, Pritam Mukherjee, Boah Kim, et al.. (2025). MRISegmenter: A Fully Accurate and Robust Automated Multiorgan and Structure Segmentation Tool for T1-weighted Abdominal MRI. Radiology. 315(1). e241979–e241979. 2 indexed citations
2.
Kim, Boah, et al.. (2024). Automated classification of body MRI sequence type using convolutional neural networks. 17–17. 3 indexed citations
3.
Kim, Boah, et al.. (2024). Automated Classification of Body MRI Sequences Using Convolutional Neural Networks. Academic Radiology. 32(3). 1192–1203. 1 indexed citations
4.
Kim, Boah, et al.. (2024). OTMorph: Unsupervised Multi-Domain Abdominal Medical Image Registration Using Neural Optimal Transport. IEEE Transactions on Medical Imaging. 44(1). 165–179. 1 indexed citations
5.
Kim, Boah, et al.. (2024). Unsupervised multi-parametric MRI registration using neural optimal transport. PubMed. 12927. 27–27. 1 indexed citations
6.
Kim, Boah, Yujin Oh, Bradford J. Wood, Ronald M. Summers, & Jong Chul Ye. (2023). C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation. Medical Image Analysis. 91. 103022–103022. 11 indexed citations
7.
Kim, Boah, et al.. (2022). Task-Agnostic Vision Transformer for Distributed Learning of Image Processing. IEEE Transactions on Image Processing. 32. 203–218. 12 indexed citations
8.
Park, Sang Joon, et al.. (2021). Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis. Neural Information Processing Systems. 34. 20 indexed citations
9.
Kim, Boah, et al.. (2021). CycleMorph: Cycle consistent unsupervised deformable image registration. Medical Image Analysis. 71. 102036–102036. 175 indexed citations breakdown →
10.
Kim, Boah, et al.. (2020). Unsupervised Deformable Image Registration Using Polyphase UNet for 3D Brain MRI Volumes. Investigative Magnetic Resonance Imaging. 24(4). 223–223. 3 indexed citations
11.
Kim, Boah & Jong Chul Ye. (2019). Multiphase Level-Set Loss for Semi-Supervised and Unsupervised Segmentation with Deep Learning. arXiv (Cornell University). 3 indexed citations
12.
Kim, Boah & Jong Chul Ye. (2018). Cycle-consistent adversarial network with polyphase U-Nets for liver lesion segmentation. 8 indexed citations
13.
Hong, Soon-Beom, Andrew Zalesky, Subin Park, et al.. (2014). COMT genotype affects brain white matter pathways in attention-deficit/hyperactivity disorder. Human Brain Mapping. 36(1). 367–377. 33 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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