Dosik Hwang

3.1k total citations · 1 hit paper
87 papers, 1.8k citations indexed

About

Dosik Hwang is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Dosik Hwang has authored 87 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Radiology, Nuclear Medicine and Imaging, 27 papers in Biomedical Engineering and 16 papers in Computer Vision and Pattern Recognition. Recurrent topics in Dosik Hwang's work include Advanced MRI Techniques and Applications (26 papers), Medical Imaging Techniques and Applications (17 papers) and Advanced Neuroimaging Techniques and Applications (17 papers). Dosik Hwang is often cited by papers focused on Advanced MRI Techniques and Applications (26 papers), Medical Imaging Techniques and Applications (17 papers) and Advanced Neuroimaging Techniques and Applications (17 papers). Dosik Hwang collaborates with scholars based in South Korea, United States and Japan. Dosik Hwang's co-authors include Jinseong Jang, Yiping P. Du, Taejoon Eo, Yohan Jun, Dong‐Hyun Kim, Taeseong Kim, Ho‐Joon Lee, Hyungseob Shin, Yoonho Nam and Hyoung Suk Park and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and NeuroImage.

In The Last Decade

Dosik Hwang

80 papers receiving 1.8k citations

Hit Papers

KIKI‐net: cross‐domain co... 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 →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Dosik Hwang 1.1k 453 247 152 146 87 1.8k
Jonathan Bishop 1.1k 0.9× 678 1.5× 131 0.5× 102 0.7× 46 0.3× 30 1.8k
Yanqiu Feng 678 0.6× 348 0.8× 303 1.2× 73 0.5× 122 0.8× 104 1.5k
Usha Sinha 1.7k 1.4× 314 0.7× 145 0.6× 128 0.8× 93 0.6× 90 2.3k
Raimo Sepponen 1.0k 0.9× 334 0.7× 175 0.7× 119 0.8× 37 0.3× 103 2.2k
Michaël Sdika 517 0.5× 152 0.3× 195 0.8× 66 0.4× 54 0.4× 45 946
Dirk H. J. Poot 1.3k 1.1× 171 0.4× 382 1.5× 146 1.0× 37 0.3× 94 1.9k
Daniel J. Blezek 1.0k 0.9× 541 1.2× 339 1.4× 45 0.3× 122 0.8× 50 1.7k
Yuyao Zhang 589 0.5× 147 0.3× 123 0.5× 206 1.4× 29 0.2× 112 1.4k
Julien Milles 959 0.8× 502 1.1× 196 0.8× 268 1.8× 43 0.3× 50 1.7k
Daniel B. Ennis 1.7k 1.5× 566 1.2× 75 0.3× 116 0.8× 16 0.1× 180 3.2k

Countries citing papers authored by Dosik Hwang

Since Specialization
Citations

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

Fields of papers citing papers by Dosik Hwang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dosik Hwang

This figure shows the co-authorship network connecting the top 25 collaborators of Dosik Hwang. A scholar is included among the top collaborators of Dosik Hwang 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 Dosik Hwang. Dosik Hwang 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, 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.
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
3.
Kang, Dong-Jin, Dosik Hwang, Beomseok Sohn, et al.. (2024). Large Language Models Are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales. Proceedings of the AAAI Conference on Artificial Intelligence. 38(16). 18417–18425. 14 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.
Dutta, Ankan, Young Uk Cho, Kyubeen Kim, et al.. (2024). Monolayer, open-mesh, pristine PEDOT:PSS-based conformal brain implants for fully MRI-compatible neural interfaces. Biosensors and Bioelectronics. 260. 116446–116446. 9 indexed citations
6.
Jun, Yohan, Yae Won Park, Hyungseob Shin, et al.. (2023). Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning. European Radiology. 33(9). 6124–6133. 8 indexed citations
7.
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
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.
Jang, Jinseong, et al.. (2020). Dynamic Range Expansion Using Cumulative Histogram Learning for High Dynamic Range Image Generation. IEEE Access. 8. 38554–38567. 14 indexed citations
11.
Park, In Yong, et al.. (2020). Deep-learned spike representations and sorting via an ensemble of auto-encoders. Neural Networks. 134. 131–142. 24 indexed citations
12.
Jang, Jinseong, et al.. (2020). Deep‐learned short tau inversion recovery imaging using multi‐contrast MR images. Magnetic Resonance in Medicine. 84(6). 2994–3008. 12 indexed citations
13.
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
14.
Kim, Taehoon, Gwangmook Kim, Taeseong Kim, et al.. (2019). Megahertz-wave-transmitting conducting polymer electrode for device-to-device integration. Nature Communications. 10(1). 653–653. 24 indexed citations
15.
Park, In Yong, et al.. (2019). Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings. Applied Sciences. 10(1). 301–301. 20 indexed citations
16.
Bae, Won C., et al.. (2018). Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines. Applied Sciences. 8(9). 1586–1586. 20 indexed citations
17.
Jang, Jinseong, et al.. (2018). Inverse Tone Mapping Operator Using Sequential Deep Neural Networks Based on the Human Visual System. IEEE Access. 6. 52058–52072. 7 indexed citations
18.
19.
Bae, Won C., et al.. (2018). Fine-Grain Segmentation of the Intervertebral Discs from MR Spine Images Using Deep Convolutional Neural Networks: BSU-Net. Applied Sciences. 8(9). 1656–1656. 41 indexed citations
20.
Bae, Won C., et al.. (2017). Semi-automatic segmentation algorithm for vertebral body in MR spine image. 대한전자공학회 학술대회. 810–812. 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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