Yeonggul Jang
Orcid: 0000-0002-5805-7494
According to our database1,
Yeonggul Jang
authored at least 24 papers
between 2011 and 2023.
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Bibliography
2023
Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising.
Comput. Biol. Medicine, June, 2023
Deep Learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction.
IEEE Trans. Medical Imaging, 2023
A Unified Approach for Comprehensive Analysis of Various Spectral and Tissue Doppler Echocardiography.
CoRR, 2023
Self supervised convolutional kernel based handcrafted feature harmonization: Enhanced left ventricle hypertension disease phenotyping on echocardiography.
CoRR, 2023
Echocardiographic View Classification with Integrated Out-of-Distribution Detection for Enhanced Automatic Echocardiographic Analysis.
CoRR, 2023
2022
Bayesian approaches for Quantifying Clinicians' Variability in Medical Image Quantification.
CoRR, 2022
Reconnection of fragmented parts of coronary arteries using local geometric features in X-ray angiography images.
Comput. Biol. Medicine, 2022
2021
Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data.
IEEE J. Biomed. Health Informatics, 2021
Deep Reinforcement Learning with Explicit Spatio-Sequential Encoding Network for Coronary Ostia Identification in CT Images.
Sensors, 2021
Multi-Scale Conditional Generative Adversarial Network for Small-Sized Lung Nodules Using Class Activation Region Influence Maximization.
IEEE Access, 2021
2020
Deep Learning Cross-Phase Style Transfer for Motion Artifact Correction in Coronary Computed Tomography Angiography.
IEEE Access, 2020
2019
Identification of coronary arteries in CT images by Bayesian analysis of geometric relations among anatomical landmarks.
Pattern Recognit., 2019
Proceedings of the 2019 Challenge on Segmentation of THoracic Organs at Risk in CT Images, 2019
2018
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
IEEE Trans. Medical Imaging, 2018
Fully Automatic Segmentation of Coronary Arteries Based on Deep Neural Network in Intravascular Ultrasound Images.
Proceedings of the Intravascular Imaging and Computer Assisted Stenting - and - Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, 2018
Deep Learning Based Coronary Artery Motion Artifact Compensation Using Style-Transfer Synthesis in CT Images.
Proceedings of the Simulation and Synthesis in Medical Imaging, 2018
Full Quantification of Left Ventricle Using Deep Multitask Network with Combination of 2D and 3D Convolution on 2D + t Cine MRI.
Proceedings of the Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges, 2018
2017
Maximum a posteriori estimation method for aorta localization and coronary seed identification.
Pattern Recognit., 2017
Proceedings of the Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges, 2017
Proceedings of the 14th IEEE International Symposium on Biomedical Imaging, 2017
2016
Comput. Math. Methods Medicine, 2016
2012
Proceedings of the Computer Applications for Bio-technology, Multimedia, and Ubiquitous City, 2012
2011
Proceedings of the Database Theory and Application, Bio-Science and Bio-Technology, 2011
Proceedings of the Software Engineering, Business Continuity, and Education, 2011