Mingyu Kim

631 total citations
14 papers, 372 citations indexed

About

Mingyu Kim is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Surgery. According to data from OpenAlex, Mingyu Kim has authored 14 papers receiving a total of 372 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 4 papers in Artificial Intelligence and 2 papers in Surgery. Recurrent topics in Mingyu Kim's work include Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (4 papers) and COVID-19 diagnosis using AI (3 papers). Mingyu Kim is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (4 papers) and COVID-19 diagnosis using AI (3 papers). Mingyu Kim collaborates with scholars based in South Korea, United Kingdom and United States. Mingyu Kim's co-authors include Namkug Kim, Hyun‐Jin Bae, Keewon Shin, Jihye Yun, Ryoungwoo Jang, Yongwon Cho, Miso Jang, Seung Hun Lee, Jung‐Min Koh and Sung Jin Bae and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Food Chemistry.

In The Last Decade

Mingyu Kim

11 papers receiving 367 citations

Peers

Mingyu Kim

RNMI

CVPR

PRM

Ryoungwoo Jang South Korea

Keewon Shin South Korea

Jiayu Huo China

Tanushree Meena India

Gengyan Zhao United States

Félix Paulano‐Godino Spain

Mutlu Demirer United States

Fuk Tang Hong Kong

Clifford Yang United States

Stephanie Leung Canada

Ryoungwoo Jang South Korea

Mingyu Kim

214 ×1.2

RNMI

113 ×1.1

124 ×1.2

51 ×1.0

CVPR

49 ×1.4

PRM

Citations per year, relative to Mingyu Kim Mingyu Kim (= 1×) peers Ryoungwoo Jang

Countries citing papers authored by Mingyu Kim

Since Specialization

Citations

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

Fields of papers citing papers by Mingyu Kim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mingyu Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Mingyu Kim. A scholar is included among the top collaborators of Mingyu 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 Mingyu Kim. Mingyu 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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14 of 14 papers shown

Kim, Mingyu, et al.. (2024). Evaluation of Extra-Prostatic Extension on Deep Learning-Reconstructed High-Resolution Thin-Slice T2-Weighted Images in Patients with Prostate Cancer. Cancers. 16(2). 413–413. 3 indexed citations

Ji, Yong Bae, Mingyu Kim, Jae Won Chang, et al.. (2024). Feasibility of active surveillance in patients with clinically T1b papillary thyroid carcinoma ≤1.5 cm in preoperative ultrasonography: MASTER study. European Thyroid Journal. 13(2).

Kim, Mingyu, Inyoung Park, Dong‐Hyeok Kim, et al.. (2024). Fabrication of agar-based tissue-mimicking phantom for the technical evaluation of biomedical optical imaging systems. Current Applied Physics. 61. 80–85. 6 indexed citations

Choi, Seungwon, et al.. (2024). Dual‐Electrolyte Neuromorphic Transistor for Risk Detection and Image Processing. Advanced Materials Technologies. 10(7).

Kim, Mingyu, et al.. (2023). Analysis of volatile and nonvolatile compounds in decaffeinated and regular coffee prepared under various roasting conditions. Food Chemistry. 435. 137543–137543. 19 indexed citations

Kim, Mingyu, et al.. (2022). Enhancing deep learning based classifiers with inpainting anatomical side markers (L/R markers) for multi-center trials. Computer Methods and Programs in Biomedicine. 220. 106705–106705. 6 indexed citations

Kim, Mingyu, Miso Jang, Jeongeun Hwang, et al.. (2022). Synthesizing realistic high-resolution retina image by style-based generative adversarial network and its utilization. Scientific Reports. 12(1). 17307–17307. 24 indexed citations

Kim, Mingyu, et al.. (2022). Bone suppression on pediatric chest radiographs via a deep learning-based cascade model. Computer Methods and Programs in Biomedicine. 215. 106627–106627. 10 indexed citations

Kim, Mingyu, et al.. (2021). Realistic high-resolution lateral cephalometric radiography generated by progressive growing generative adversarial network and quality evaluations. Scientific Reports. 11(1). 12563–12563. 17 indexed citations

10.

Jang, Miso, Mingyu Kim, Sung Jin Bae, et al.. (2020). Opportunistic Osteoporosis Screening Using Chest Radiographs With Deep Learning: Development and External Validation With a Cohort Dataset. Journal of Bone and Mineral Research. 37(2). 369–377. 56 indexed citations

11.

Kim, Yong‐Chan, et al.. (2020). Radiographic and clinical evidence: osteoarthritic knee can change surgical result for lumbar degenerative disease patient undergone surgery for 3-year follow-up: a retrospective comparative clinical study. BMC Musculoskeletal Disorders. 21(1). 740–740. 2 indexed citations

12.

Kim, Mingyu & Hyun‐Jin Bae. (2020). Data Augmentation Techniques for Deep Learning-Based Medical Image Analyses. SHILAP Revista de lepidopterología. 81(6). 1290–1290. 5 indexed citations

13.

Kim, Mingyu, Jihye Yun, Yongwon Cho, et al.. (2019). Deep Learning in Medical Imaging. Neurospine. 16(4). 657–668. 224 indexed citations

14.

Kim, Mingyu, et al.. (2018). A study on the usefulness of method using Wide volume axial scan and assist device without sedation in pediatric brain CT. 20(1). 11–22.

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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