Dong Hye Ye

7.7k total citations
53 papers, 787 citations indexed

About

Dong Hye Ye is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Dong Hye Ye has authored 53 papers receiving a total of 787 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Radiology, Nuclear Medicine and Imaging, 19 papers in Computer Vision and Pattern Recognition and 18 papers in Biomedical Engineering. Recurrent topics in Dong Hye Ye's work include Medical Imaging Techniques and Applications (13 papers), Medical Image Segmentation Techniques (9 papers) and Advanced X-ray and CT Imaging (8 papers). Dong Hye Ye is often cited by papers focused on Medical Imaging Techniques and Applications (13 papers), Medical Image Segmentation Techniques (9 papers) and Advanced X-ray and CT Imaging (8 papers). Dong Hye Ye collaborates with scholars based in United States, South Korea and India. Dong Hye Ye's co-authors include Charles A. Bouman, Christos Davatzikos, Juan Wachs, Jihun Hamm, Mathias Kölsch, Kilian M. Pohl, Ragini Verma, Jean‐Baptiste Thibault, Timothy H. Chung and K. Sauer and has published in prestigious journals such as Analytical Chemistry, IEEE Transactions on Medical Imaging and Sensors.

In The Last Decade

Dong Hye Ye

49 papers receiving 757 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dong Hye Ye United States	17	337	248	146	141	112	53	787
D. Goldgof United States	12	561 1.7×	197 0.8×	54 0.4×	94 0.7×	135 1.2×	16	873
HyunWook Park South Korea	19	527 1.6×	532 2.1×	114 0.8×	28 0.2×	46 0.4×	95	1.2k
Oleh J. Tretiak United States	15	400 1.2×	346 1.4×	272 1.9×	41 0.3×	96 0.9×	44	943
William H. Nailon United Kingdom	13	239 0.7×	195 0.8×	143 1.0×	13 0.1×	94 0.8×	38	782
Chengcai Leng China	14	303 0.9×	83 0.3×	84 0.6×	110 0.8×	91 0.8×	62	574
David Tucker United States	6	700 2.1×	164 0.7×	118 0.8×	80 0.6×	155 1.4×	11	953
Richard McKinley Switzerland	14	92 0.3×	194 0.8×	137 0.9×	88 0.6×	85 0.8×	50	668
Gerardo Hermosillo United States	13	366 1.1×	477 1.9×	123 0.8×	85 0.6×	44 0.4×	25	881
Du‐Yih Tsai Japan	14	317 0.9×	485 2.0×	408 2.8×	67 0.5×	188 1.7×	59	1.2k

Countries citing papers authored by Dong Hye Ye

Since Specialization

Citations

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

Fields of papers citing papers by Dong Hye Ye

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dong Hye Ye. 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 Dong Hye Ye. The network helps show where Dong Hye Ye may publish in the future.

Co-authorship network of co-authors of Dong Hye Ye

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

All Works

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20 of 20 papers shown

Shaik, Nagur Shareef, et al.. (2025). Dynamic Contextual Attention Network: Transforming Spatial Representations into Adaptive Insights for Endoscopic Polyp Diagnosis. PubMed. 2025. 1–6.

Shaik, Nagur Shareef, et al.. (2025). GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioning. 1–5. 1 indexed citations

Lu, Tongtong, Julie M. Jorns, Taly Gilat Schmidt, et al.. (2023). Deep learning classification of deep ultraviolet fluorescence images toward intra-operative margin assessment in breast cancer. Frontiers in Oncology. 13. 1179025–1179025. 4 indexed citations

Hu, Hang, et al.. (2023). Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging. IEEE Transactions on Computational Imaging. 9. 250–259. 2 indexed citations

Hu, Hang, et al.. (2022). High-Throughput Mass Spectrometry Imaging with Dynamic Sparse Sampling. ACS Measurement Science Au. 2(5). 466–474. 22 indexed citations

Hu, Hang, et al.. (2021). Deep Learning Approach for Dynamic Sparse Sampling for High-Throughput Mass Spectrometry Imaging. Electronic Imaging. 33(15). 290–1. 13 indexed citations

Schmidt, Taly Gilat, et al.. (2021). Enhancing reproductive organ segmentation in pediatric CT via adversarial learning. PubMed. 11596. 31–31. 6 indexed citations

Lu, Tongtong, Julie M. Jorns, Todd C. Doehring, et al.. (2020). Rapid assessment of breast tumor margins using deep ultraviolet fluorescence scanning microscopy. Journal of Biomedical Optics. 25(12). 13 indexed citations

Zhang, Shijie, Dong Hye Ye, Azhad U. Chowdhury, et al.. (2018). Dynamic Sparse Sampling for Confocal Raman Microscopy. Analytical Chemistry. 90(7). 4461–4469. 22 indexed citations

10.

Ye, Dong Hye, et al.. (2018). Prior-Guided Metal Artifact Reduction for Iterative X-Ray Computed Tomography. IEEE Transactions on Medical Imaging. 38(6). 1532–1542. 23 indexed citations

11.

Ye, Dong Hye, et al.. (2018). Deep Residual Learning for Model-Based Iterative CT Reconstruction Using Plug-and-Play Framework. 6668–6672. 19 indexed citations

12.

Chowdhury, Azhad U., Dong Hye Ye, Shijie Zhang, et al.. (2017). Second Harmonic Generation Guided Raman Spectroscopy for Sensitive Detection of Polymorph Transitions. Analytical Chemistry. 89(11). 5958–5965. 11 indexed citations

13.

Ye, Dong Hye, et al.. (2017). A Framework for Dynamic Image Sampling Based on Supervised Learning. IEEE Transactions on Computational Imaging. 4(1). 1–16. 18 indexed citations

14.

Ye, Dong Hye, et al.. (2017). A Model Based Neuron Detection Approach using Sparse Location Priors. Electronic Imaging. 29(17). 10–17. 3 indexed citations

15.

Ye, Dong Hye, et al.. (2016). Multi-target detection and tracking from a single camera in Unmanned Aerial Vehicles (UAVs). 4992–4997. 96 indexed citations

16.

Ye, Dong Hye, et al.. (2012). Regional Manifold Learning for Deformable Registration of Brain MR Images. Lecture notes in computer science. 15(Pt 3). 131–138. 16 indexed citations

17.

Ou, Yangming, Dong Hye Ye, Kilian M. Pohl, & Christos Davatzikos. (2012). Validation of DRAMMS among 12 Popular Methods in Cross-Subject Cardiac MRI Registration. Lecture notes in computer science. 7359. 209–219. 10 indexed citations

18.

Konukoğlu, Ender, Ben Glocker, Dong Hye Ye, Antonio Criminisi, & Kilian M. Pohl. (2012). Discriminative Segmentation-Based Evaluation Through Shape Dissimilarity. IEEE Transactions on Medical Imaging. 31(12). 2278–2289. 5 indexed citations

19.

Ye, Dong Hye, Harold Litt, Christos Davatzikos, & Kilian M. Pohl. (2011). Morphological Classification: Application to Cardiac MRI of Tetralogy of Fallot. Lecture notes in computer science. 6666. 180–187. 7 indexed citations

20.

Hamm, Jihun, Dong Hye Ye, Ragini Verma, & Christos Davatzikos. (2010). GRAM: A framework for geodesic registration on anatomical manifolds. Medical Image Analysis. 14(5). 633–642. 82 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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