Beomhee Park

2.0k total citations
16 papers, 543 citations indexed

About

Beomhee Park is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Beomhee Park has authored 16 papers receiving a total of 543 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Pulmonary and Respiratory Medicine and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Beomhee Park's work include COVID-19 diagnosis using AI (9 papers), Lung Cancer Diagnosis and Treatment (8 papers) and Radiomics and Machine Learning in Medical Imaging (7 papers). Beomhee Park is often cited by papers focused on COVID-19 diagnosis using AI (9 papers), Lung Cancer Diagnosis and Treatment (8 papers) and Radiomics and Machine Learning in Medical Imaging (7 papers). Beomhee Park collaborates with scholars based in South Korea. Beomhee Park's co-authors include Namkug Kim, Joon Beom Seo, Sang Min Lee, Yongwon Cho, Jihye Yun, Hyun‐Jin Bae, Sang Min Lee, Jongha Park, Hee-Jun Park and Minho Lee and has published in prestigious journals such as Scientific Reports, Radiology and Computer Methods and Programs in Biomedicine.

In The Last Decade

Beomhee Park

16 papers receiving 531 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Beomhee Park South Korea	12	299	218	124	81	76	16	543
Johannes Hofmanninger Austria	11	385 1.3×	229 1.1×	129 1.0×	30 0.4×	68 0.9×	14	564
Florian Prayer Austria	11	413 1.4×	240 1.1×	100 0.8×	43 0.5×	45 0.6×	36	646
Sila Kurugol United States	14	244 0.8×	292 1.3×	62 0.5×	78 1.0×	70 0.9×	51	684
Saikit Lam Hong Kong	17	488 1.6×	198 0.9×	133 1.1×	40 0.5×	40 0.5×	53	749
Jihye Yun South Korea	15	564 1.9×	323 1.5×	190 1.5×	58 0.7×	110 1.4×	36	985
Dooman Arefan United States	14	418 1.4×	138 0.6×	273 2.2×	80 1.0×	42 0.6×	43	597
Kohei Murao Japan	10	469 1.6×	400 1.8×	164 1.3×	38 0.5×	95 1.3×	21	679
Dakai Jin United States	15	370 1.2×	138 0.6×	154 1.2×	31 0.4×	208 2.7×	35	697
Simon Köhl Germany	7	418 1.4×	315 1.4×	147 1.2×	18 0.2×	111 1.5×	11	585
Seyoun Park United States	13	461 1.5×	179 0.8×	141 1.1×	205 2.5×	165 2.2×	30	676

Countries citing papers authored by Beomhee Park

Since Specialization

Citations

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

Fields of papers citing papers by Beomhee Park

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Beomhee Park

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

All Works

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16 of 16 papers shown

Park, Beomhee, et al.. (2022). Utilizing Synthetic Nodules for Improving Nodule Detection in Chest Radiographs. Journal of Digital Imaging. 35(4). 1061–1068. 6 indexed citations

Cho, Yongwon, Beomhee Park, Sang Min Lee, et al.. (2021). Optimal number of strong labels for curriculum learning with convolutional neural network to classify pulmonary abnormalities in chest radiographs. Computers in Biology and Medicine. 136. 104750–104750. 8 indexed citations

Park, Sohee, Sang Min Lee, Woong Bae, et al.. (2021). Added Value of Deep Learning–based Detection System for Multiple Major Findings on Chest Radiographs: A Randomized Crossover Study. Radiology. 299(2). 450–459. 53 indexed citations

Choi, Gwang Hyeon, Jihye Yun, Jonggi Choi, et al.. (2020). Development of machine learning-based clinical decision support system for hepatocellular carcinoma. Scientific Reports. 10(1). 14855–14855. 32 indexed citations

Song, Eun Mi, Beomhee Park, Sung Wook Hwang, et al.. (2020). Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model. Scientific Reports. 10(1). 30–30. 78 indexed citations

Hwang, Hye Jeon, Joon Beom Seo, Sang Min Lee, et al.. (2020). Content-Based Image Retrieval of Chest CT with Convolutional Neural Network for Diffuse Interstitial Lung Disease: Performance Assessment in Three Major Idiopathic Interstitial Pneumonias. Korean Journal of Radiology. 22(2). 281–281. 24 indexed citations

Park, Beomhee, et al.. (2020). CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels. Computer Methods and Programs in Biomedicine. 196. 105615–105615. 6 indexed citations

Kim, Taehun, Sungwon Ham, Beomhee Park, et al.. (2020). Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT. Scientific Reports. 10(1). 366–366. 39 indexed citations

Seo, Dong‐Woo, Beomhee Park, Youn‐Jung Kim, et al.. (2020). Prediction of Adverse Events in Stable Non-Variceal Gastrointestinal Bleeding Using Machine Learning. Journal of Clinical Medicine. 9(8). 2603–2603. 13 indexed citations

10.

Cho, Yongwon, Sang Min Lee, June‐Goo Lee, et al.. (2020). Deep chest X‐ray: Detection and classification of lesions based on deep convolutional neural networks. International Journal of Imaging Systems and Technology. 31(1). 72–81. 6 indexed citations

11.

Park, Jongha, Jihye Yun, Namkug Kim, et al.. (2019). Fully Automated Lung Lobe Segmentation in Volumetric Chest CT with 3D U-Net: Validation with Intra- and Extra-Datasets. Journal of Digital Imaging. 33(1). 221–230. 82 indexed citations

12.

Park, Beomhee, et al.. (2019). Lung Segmentation on HRCT and Volumetric CT for Diffuse Interstitial Lung Disease Using Deep Convolutional Neural Networks. Journal of Digital Imaging. 32(6). 1019–1026. 90 indexed citations

13.

Kim, Young‐Gon, Heounjeong Go, Yongwon Cho, et al.. (2019). A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides. Scientific Reports. 9(1). 5123–5123. 21 indexed citations

14.

Park, Beomhee, Yongwon Cho, Gaeun Lee, et al.. (2019). A Curriculum Learning Strategy to Enhance the Accuracy of Classification of Various Lesions in Chest-PA X-ray Screening for Pulmonary Abnormalities. Scientific Reports. 9(1). 15352–15352. 19 indexed citations

15.

Bae, Hyun‐Jin, Namju Kim, Beomhee Park, et al.. (2018). A Perlin Noise-Based Augmentation Strategy for Deep Learning with Small Data Samples of HRCT Images. Scientific Reports. 8(1). 17687–17687. 52 indexed citations

16.

Jun, Sanghoon, Beomhee Park, Joon Beom Seo, Sang‐Min Lee, & Namkug Kim. (2017). Development of a Computer-Aided Differential Diagnosis System to Distinguish Between Usual Interstitial Pneumonia and Non-specific Interstitial Pneumonia Using Texture- and Shape-Based Hierarchical Classifiers on HRCT Images. Journal of Digital Imaging. 31(2). 235–244. 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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