| Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? O Bernard, A Lalande, C Zotti, F Cervenansky, X Yang, PA Heng, I Cetin, ... IEEE transactions on medical imaging 37 (11), 2514-2525, 2018 | 1691 | 2018 |
| Automatic segmentation of LV and RV in cardiac MRI Y Jang, Y Hong, S Ha, S Kim, HJ Chang Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS …, 2018 | 94 | 2018 |
| Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images S Kim, Y Jang, B Jeon, Y Hong, H Shim, H Chang Intravascular Imaging and Computer Assisted Stenting and Large-Scale …, 2018 | 49 | 2018 |
| Automatic aortic valve landmark localization in coronary CT angiography using colonial walk WA Al, HY Jung, ID Yun, Y Jang, HB Park, HJ Chang PloS one 13 (7), e0200317, 2018 | 37 | 2018 |
| Automatic coronary artery segmentation using active search for branches and seemingly disconnected vessel segments from coronary CT angiography D Han, H Shim, B Jeon, Y Jang, Y Hong, S Jung, S Ha, HJ Chang PLoS One 11 (8), e0156837, 2016 | 36 | 2016 |
| Quantitative assessment of foot blood flow by using dynamic volume perfusion CT technique: a feasibility study S Hur, HJ Jae, Y Jang, SK Min, SI Min, DY Lee, SG Seo, HC Kim, ... Radiology 279 (1), 195-206, 2016 | 30 | 2016 |
| Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges: 9th International Workshop, STACOM 2018, Held in Conjunction … M Pop, M Sermesant, J Zhao, S Li, K McLeod, A Young, K Rhode, T Mansi Springer, 2019 | 29 | 2019 |
| Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges: 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City … M Pop, M Sermesant, PM Jodoin, A Lalande, X Zhuang, G Yang, A Young, ... Springer, 2018 | 23* | 2018 |
| A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA D Han, NT Doan, H Shim, B Jeon, H Lee, Y Hong, HJ Chang Computer methods and programs in biomedicine 117 (2), 179-188, 2014 | 21 | 2014 |
| Deep learning on multiphysical features and hemodynamic modeling for abdominal aortic aneurysm growth prediction S Kim, Z Jiang, BA Zambrano, Y Jang, S Baek, S Yoo, HJ Chang IEEE Transactions on Medical Imaging 42 (1), 196-208, 2022 | 18 | 2022 |
| Deep learning cross-phase style transfer for motion artifact correction in coronary computed tomography angiography S Jung, S Lee, B Jeon, Y Jang, HJ Chang IEEE Access 8, 81849-81863, 2020 | 18 | 2020 |
| A cascaded two-step approach for segmentation of thoracic organs S Kim, Y Jang, K Han, H Shim, HJ Chang CEUR Workshop Proceedings 2349, 2019 | 13 | 2019 |
| Spinal bone bruise: can computed tomography (CT) enable accurate diagnosis? D Na, SJ Hong, MA Yoon, KS Ahn, CH Kang, BH Kim, Y Jang Academic Radiology 23 (11), 1376-1383, 2016 | 13 | 2016 |
| Maximum a posteriori estimation method for aorta localization and coronary seed identification B Jeon, Y Hong, D Han, Y Jang, S Jung, Y Hong, S Ha, H Shim, ... Pattern Recognition 68, 222-232, 2017 | 12 | 2017 |
| Multi-scale conditional generative adversarial network for small-sized lung nodules using class activation region influence maximization K Ann, Y Jang, H Shim, HJ Chang IEEE Access 9, 139426-139437, 2021 | 11 | 2021 |
| Geodesic distance algorithm for extracting the ascending aorta from 3D CT images Y Jang, HY Jung, Y Hong, I Cho, H Shim, HJ Chang Computational and mathematical methods in medicine 2016 (1), 4561979, 2016 | 11 | 2016 |
| Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising J Lee, J Jeon, Y Hong, D Jeong, Y Jang, B Jeon, HJ Baek, E Cho, H Shim, ... Computers in Biology and Medicine 159, 106931, 2023 | 8 | 2023 |
| Deep reinforcement learning with explicit spatio-sequential encoding network for coronary ostia identification in CT images Y Jang, B Jeon Sensors 21 (18), 6187, 2021 | 8 | 2021 |
| Left ventricle quantification challenge: A comprehensive comparison and evaluation of segmentation and regression for mid-ventricular short-axis cardiac MR data W Xue, J Li, Z Hu, E Kerfoot, J Clough, I Oksuz, H Xu, V Grau, F Guo, ... IEEE journal of biomedical and health informatics 25 (9), 3541-3553, 2021 | 8 | 2021 |
| Identification of coronary arteries in CT images by Bayesian analysis of geometric relations among anatomical landmarks B Jeon, Y Jang, H Shim, HJ Chang Pattern Recognition 96, 106958, 2019 | 8 | 2019 |
Byunghwan JeonHankuk University of Foreign Studies在 yonsei.ac.kr 的电子邮件经过验证
