Hwiyoung Kim

1.5k total citations
42 papers, 902 citations indexed

About

Hwiyoung Kim is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Hwiyoung Kim has authored 42 papers receiving a total of 902 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Radiology, Nuclear Medicine and Imaging, 15 papers in Biomedical Engineering and 8 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Hwiyoung Kim's work include Radiomics and Machine Learning in Medical Imaging (19 papers), Advanced X-ray and CT Imaging (8 papers) and Glioma Diagnosis and Treatment (7 papers). Hwiyoung Kim is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (19 papers), Advanced X-ray and CT Imaging (8 papers) and Glioma Diagnosis and Treatment (7 papers). Hwiyoung Kim collaborates with scholars based in South Korea, United States and Russia. Hwiyoung Kim's co-authors include Sung Soo Ahn, Seung‐Koo Lee, Kyunghwa Han, Yae Won Park, Dong Wook Kim, Chansik An, Hyung Jun Kim, Woong Nam, In–Ho Cha and Ji Eun Park and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and The Journal of Clinical Endocrinology & Metabolism.

In The Last Decade

Hwiyoung Kim

41 papers receiving 886 citations

Peers

Hwiyoung Kim

RNMI

GENET

PRM

David Zopfs Germany

Kai Roman Laukamp Germany

Manoj Mannil Switzerland

Jay Patel United States

Jiangfen Wu China

Kyong Joon Lee South Korea

Lenhard Pennig Germany

Giancarlo Oliva Italy

Sergey Primakov Netherlands

P. Meyer France

David Zopfs Germany

Hwiyoung Kim

669 ×1.4

RNMI

544 ×2.5

79 ×0.4

GENET

151 ×1.1

PRM

Citations per year, relative to Hwiyoung Kim Hwiyoung Kim (= 1×) peers David Zopfs

Countries citing papers authored by Hwiyoung Kim

Since Specialization

Citations

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

Fields of papers citing papers by Hwiyoung Kim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hwiyoung Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Hwiyoung Kim. A scholar is included among the top collaborators of Hwiyoung 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 Hwiyoung Kim. Hwiyoung Kim 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

Suh, Young Joo, Kyunghwa Han, Hwiyoung Kim, et al.. (2024). Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas. Yonsei Medical Journal. 65(3). 163–163. 7 indexed citations

Hwang, Eui Jin, Ji Eun Park, Kyoung Doo Song, et al.. (2024). 2023 Survey on User Experience of Artificial Intelligence Software in Radiology by the Korean Society of Radiology. Korean Journal of Radiology. 25(7). 613–613. 6 indexed citations

Kim, Jae-Won, et al.. (2024). Uncertainty Quantification in Automated Detection of Vertebral Metastasis Using Ensemble Monte Carlo Dropout. Journal of Imaging Informatics in Medicine. 38(5). 2700–2715. 1 indexed citations

Ra, Yoonsang, Hwiyoung Kim, Kyungwho Choi, et al.. (2024). Recovered graphene-hydrogel nanocomposites for multi-modal human motion recognition via optimized triboelectrification and machine learning. Composites Part B Engineering. 291. 111997–111997. 13 indexed citations

Han, Yoon Dae, et al.. (2023). Development of a deep learning based image processing tool for enhanced organoid analysis. Scientific Reports. 13(1). 19841–19841. 18 indexed citations

Chang, Suyon, et al.. (2023). T1 Map-Based Radiomics for Prediction of Left Ventricular Reverse Remodeling in Patients With Nonischemic Dilated Cardiomyopathy. Korean Journal of Radiology. 24(5). 395–395. 8 indexed citations

Kim, Hwiyoung, Kye Ho Lee, Kyunghwa Han, et al.. (2023). Development and Validation of a Deep Learning–Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs. JAMA Network Open. 6(1). e2253820–e2253820. 9 indexed citations

Kim, Sung Jun, et al.. (2023). Detection of Pacemaker and Identification of MRI-conditional Pacemaker Based on Deep-learning Convolutional Neural Networks to Improve Patient Safety. Journal of Medical Systems. 47(1). 80–80. 1 indexed citations

Suh, Young Joo, et al.. (2023). Influence of computed tomography slice thickness on deep learning-based, automatic coronary artery calcium scoring software performance. Quantitative Imaging in Medicine and Surgery. 13(7). 4257–4267. 4 indexed citations

10.

Joo, Bio, Sung Soo Ahn, Chansik An, et al.. (2022). Fully automated radiomics-based machine learning models for multiclass classification of single brain tumors: Glioblastoma, lymphoma, and metastasis. Journal of Neuroradiology. 50(4). 388–395. 20 indexed citations

11.

Kim, Kee‐Deog, et al.. (2022). Development and Validation of a Visually Explainable Deep Learning Model for Classification of C-shaped Canals of the Mandibular Second Molars in Periapical and Panoramic Dental Radiographs. Journal of Endodontics. 48(7). 914–921. 32 indexed citations

12.

Park, Chae Jung, Kyunghwa Han, Hwiyoung Kim, et al.. (2021). MRI Features May Predict Molecular Features of Glioblastoma in Isocitrate Dehydrogenase Wild-Type Lower-Grade Gliomas. American Journal of Neuroradiology. 42(3). 448–456. 37 indexed citations

13.

Park, Yae Won, Ji Eun Park, Sung Soo Ahn, et al.. (2021). Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation. Scientific Reports. 11(1). 2913–2913. 33 indexed citations

14.

Kim, Jae‐Young, Dong Wook Kim, Kug Jin Jeon, Hwiyoung Kim, & Jong‐Ki Huh. (2021). Using deep learning to predict temporomandibular joint disc perforation based on magnetic resonance imaging. Scientific Reports. 11(1). 6680–6680. 26 indexed citations

15.

Kim, Hyung Jun, In–Ho Cha, Young‐Soo Jung, et al.. (2020). Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs. Journal of Clinical Medicine. 9(6). 1839–1839. 123 indexed citations

16.

Hong, Namki, Sung Jae Shin, Seunghyun Lee, et al.. (2020). Deep-Learning-Based Detection of Vertebral Fracture and Osteoporosis Using Lateral Spine X-Ray Radiography. Journal of Bone and Mineral Research. 38(6). 887–895. 39 indexed citations

17.

Park, Yae Won, Song E. Kim, Kyunghwa Han, et al.. (2020). Radiomics features of hippocampal regions in magnetic resonance imaging can differentiate medial temporal lobe epilepsy patients from healthy controls. Scientific Reports. 10(1). 19567–19567. 27 indexed citations

18.

Park, Yae Won, Chansik An, Jae‐Seong Lee, et al.. (2020). Diffusion tensor and postcontrast T1-weighted imaging radiomics to differentiate the epidermal growth factor receptor mutation status of brain metastases from non-small cell lung cancer. Neuroradiology. 63(3). 343–352. 31 indexed citations

19.

Bae, Sohi, Chansik An, Sung Soo Ahn, et al.. (2020). Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation. Scientific Reports. 10(1). 12110–12110. 78 indexed citations

20.

Lee, Dong‐Hoon, Hwiyoung Kim, Byoung Wook Choi, & Hee-Joung Kim. (2019). Development of a deep neural network for generating synthetic dual-energy chest x-ray images with single x-ray exposure. Physics in Medicine and Biology. 64(11). 115017–115017. 14 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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