Yeonggul Jang - IEEE Xplore Author Profile
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Yeonggul Jang

Also published under: Y. Jang

Publications
8
Citations
1,223
Publications by Year
20172024
Co-Authors:
Show All Co-Authors (85)
Image of author Yeonggul Jang

Yeonggul Jang

Also published under: Y. Jang

Affiliation

Ontact Health, Seoul, South Korea

Publication Topics

Biography

Yeonggul Jang received the B.S. degree in computer science engineering from the Hankuk University of Foreign Studies, South Korea, in 2014. He is currently pursuing the Ph.D. degree in medical science with Yonsei University, Seoul, South Korea. Since 2012, he has been a Researcher with the Medical Image Computing and Analysis Laboratory, CONNECT-AI Research Center. His research interests include the application of computer vision techniques, from geometric analysis, image processing, and deep learning to segmentation and localization in medical images.(Based on document published on 30 April 2020).
Publications
8
Citations
1,223
Publications by Year
20172024
Co-Authors:
Show All Co-Authors (85)
Author's Published Works

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A Unified Approach For Comprehensive Analysis of Various Spectral and Tissue Doppler Echocardiography

Jaeik Jeon;Jiyeon Kim;Yeonggul Jang;Yeonyee E. Yoon;Dawun Jeong;Youngtaek Hong;Seung-Ah Lee;Hyuk-Jae Chang

2024 IEEE International Symposium on Biomedical Imaging (ISBI)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Doppler echocardiography offers critical insights into cardiac function and phases by quantifying blood flow velocities and evaluating myocardial motion. However, previous methods for automating Doppler analysis, ranging from initial signal processing techniques to advanced deep learning approaches, have been constrained by their reliance on electrocardiogram (ECG) data and their inability to proc...Show More

A Unified Approach For Comprehensive Analysis of Various Spectral and Tissue Doppler Echocardiography

Jaeik Jeon;Jiyeon Kim;Yeonggul Jang;Yeonyee E. Yoon;Dawun Jeong;Youngtaek Hong;Seung-Ah Lee;Hyuk-Jae Chang

Year: 2024 | Conference Paper |

Fully Convolutional Hybrid Fusion Network With Heterogeneous Representations for Identification of S1 and S2 From Phonocardiogram

Yeonggul Jang;Juyeong Jung;Youngtaek Hong;Jina Lee;Hyunseok Jeong;Hackjoon Shim;Hyuk-Jae Chang

IEEE Journal of Biomedical and Health Informatics
Year: 2024 | Volume: 28, Issue: 12 | Journal Article |
Heart auscultation is a simple and inexpensive first-line diagnostic test for the early screening of heart abnormalities. A phonocardiogram (PCG) is a digital recording of an analog heart sound acquired using an electronic stethoscope. A computerized algorithm for PCG analysis can aid in detecting abnormal signal patterns and support the clinical use of auscultation. It is important to detect fund...Show More

Fully Convolutional Hybrid Fusion Network With Heterogeneous Representations for Identification of S1 and S2 From Phonocardiogram

Yeonggul Jang;Juyeong Jung;Youngtaek Hong;Jina Lee;Hyunseok Jeong;Hackjoon Shim;Hyuk-Jae Chang

Year: 2024 | Volume: 28, Issue: 12 | Journal Article |

Deep Learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction

Sekeun Kim;Zhenxiang Jiang;Byron A. Zambrano;Yeonggul Jang;Seungik Baek;Sunkook Yoo;Hyuk-Jae Chang

IEEE Transactions on Medical Imaging
Year: 2023 | Volume: 42, Issue: 1 | Journal Article |
Cited by: Papers (14)
Prediction of abdominal aortic aneurysm (AAA) growth is of essential importance for the early treatment and surgical intervention of AAA. Capturing key features of vascular growth, such as blood flow and intraluminal thrombus (ILT) accumulation play a crucial role in uncovering the intricated mechanism of vascular adaptation, which can ultimately enhance AAA growth prediction capabilities. However...Show More

Deep Learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction

Sekeun Kim;Zhenxiang Jiang;Byron A. Zambrano;Yeonggul Jang;Seungik Baek;Sunkook Yoo;Hyuk-Jae Chang

Year: 2023 | Volume: 42, Issue: 1 | Journal Article |

Multi-Scale Conditional Generative Adversarial Network for Small-Sized Lung Nodules Using Class Activation Region Influence Maximization

Kyeongjin Ann;Yeonggul Jang;Hackjoon Shim;Hyuk-Jae Chang

IEEE Access
Year: 2021 | Volume: 9 | Journal Article |
Cited by: Papers (6)
Automatic detection and classification of thoracic diseases using deep learning algorithms have many applications supporting radiologists’ diagnosis and prognosis. However, in the medical field, the class-imbalanced problem is extremely common due to the differences in prevalence among diseases, making it difficult to develop these applications. Many GAN-based methods have been proposed to solve t...Show More

Multi-Scale Conditional Generative Adversarial Network for Small-Sized Lung Nodules Using Class Activation Region Influence Maximization

Kyeongjin Ann;Yeonggul Jang;Hackjoon Shim;Hyuk-Jae Chang

Year: 2021 | Volume: 9 | Journal Article |

Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data

Wufeng Xue;Jiahui Li;Zhiqiang Hu;Eric Kerfoot;James Clough;Ilkay Oksuz;Hao Xu;Vicente Grau;Fumin Guo;Matthew Ng;Xiang Li;Quanzheng Li;Lihong Liu;Jin Ma;Elias Grinias;Georgios Tziritas;Wenjun Yan;Angélica Atehortúa;Mireille Garreau;Yeonggul Jang;Alejandro Debus;Enzo Ferrante;Guanyu Yang;Tiancong Hua;Shuo Li

IEEE Journal of Biomedical and Health Informatics
Year: 2021 | Volume: 25, Issue: 9 | Journal Article |
Cited by: Papers (10)
Automatic quantification of the left ventricle (LV) from cardiac magnetic resonance (CMR) images plays an important role in making the diagnosis procedure efficient, reliable, and alleviating the laborious reading work for physicians. Considerable efforts have been devoted to LV quantification using different strategies that include segmentation-based (SG) methods and the recent direct regression ...Show More

Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data

Wufeng Xue;Jiahui Li;Zhiqiang Hu;Eric Kerfoot;James Clough;Ilkay Oksuz;Hao Xu;Vicente Grau;Fumin Guo;Matthew Ng;Xiang Li;Quanzheng Li;Lihong Liu;Jin Ma;Elias Grinias;Georgios Tziritas;Wenjun Yan;Angélica Atehortúa;Mireille Garreau;Yeonggul Jang;Alejandro Debus;Enzo Ferrante;Guanyu Yang;Tiancong Hua;Shuo Li

Year: 2021 | Volume: 25, Issue: 9 | Journal Article |

Deep Learning Cross-Phase Style Transfer for Motion Artifact Correction in Coronary Computed Tomography Angiography

Sunghee Jung;Soochahn Lee;Byunghwan Jeon;Yeonggul Jang;Hyuk-Jae Chang

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (14)
Motion artifacts may occur in coronary computed tomography angiography (CCTA) due to the heartbeat and impede the clinician’s diagnosis of coronary arterial diseases. Thus, motion artifact correction of the coronary artery is required to quantify the risk of disease more accurately. We present a novel method based on deep learning for motion artifact correction in CCTA. Because the image of the co...Show More

Deep Learning Cross-Phase Style Transfer for Motion Artifact Correction in Coronary Computed Tomography Angiography

Sunghee Jung;Soochahn Lee;Byunghwan Jeon;Yeonggul Jang;Hyuk-Jae Chang

Year: 2020 | Volume: 8 | Journal Article |

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky;Xin Yang;Pheng-Ann Heng;Irem Cetin;Karim Lekadir;Oscar Camara;Miguel Angel Gonzalez Ballester;Gerard Sanroma;Sandy Napel;Steffen Petersen;Georgios Tziritas;Elias Grinias;Mahendra Khened;Varghese Alex Kollerathu;Ganapathy Krishnamurthi;Marc-Michel Rohé;Xavier Pennec;Maxime Sermesant;Fabian Isensee;Paul Jäger;Klaus H. Maier-Hein;Peter M. Full;Ivo Wolf;Sandy Engelhardt;Christian F. Baumgartner;Lisa M. Koch;Jelmer M. Wolterink;Ivana Išgum;Yeonggul Jang;Yoonmi Hong;Jay Patravali;Shubham Jain;Olivier Humbert;Pierre-Marc Jodoin

IEEE Transactions on Medical Imaging
Year: 2018 | Volume: 37, Issue: 11 | Journal Article |
Cited by: Papers (1176)
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the large...Show More

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky;Xin Yang;Pheng-Ann Heng;Irem Cetin;Karim Lekadir;Oscar Camara;Miguel Angel Gonzalez Ballester;Gerard Sanroma;Sandy Napel;Steffen Petersen;Georgios Tziritas;Elias Grinias;Mahendra Khened;Varghese Alex Kollerathu;Ganapathy Krishnamurthi;Marc-Michel Rohé;Xavier Pennec;Maxime Sermesant;Fabian Isensee;Paul Jäger;Klaus H. Maier-Hein;Peter M. Full;Ivo Wolf;Sandy Engelhardt;Christian F. Baumgartner;Lisa M. Koch;Jelmer M. Wolterink;Ivana Išgum;Yeonggul Jang;Yoonmi Hong;Jay Patravali;Shubham Jain;Olivier Humbert;Pierre-Marc Jodoin

Year: 2018 | Volume: 37, Issue: 11 | Journal Article |

Coronary luminal and wall mask prediction using convolutional neural network

Y. Hong;Y-M. Hong;Y. Jang;S. Kim;B. Jeon;S. Jung;S. Ha;D. Han;H. Shim;H. J. Chang

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
Year: 2017 | Conference Paper |
Cited by: Papers (2)
A significant amount of research has been done on the segmentation of coronary arteries. However, the resulting automated boundary delineation is still not suitable for clinical utilization. The convolutional neural network was driving advances in the medical image processing. We propose the brief convolutional network (BCN) that automatically produces the labeled mask with the luminal and wall bo...Show More

Coronary luminal and wall mask prediction using convolutional neural network

Y. Hong;Y-M. Hong;Y. Jang;S. Kim;B. Jeon;S. Jung;S. Ha;D. Han;H. Shim;H. J. Chang

Year: 2017 | Conference Paper |

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