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ORCIDJoon Yul Choi
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2020 – today
- 2024
[j11]Joon Yul Choi, Hyungsu Kim, Jin Kuk Kim, In Sik Lee, Ik Hee Ryu, Jung Soo Kim, Tae Keun Yoo
:
Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era. Medical Biol. Eng. Comput. 62(2): 449-463 (2024)- [j10]Joon Yul Choi, Ik Hee Ryu, Jin Kuk Kim, In Sik Lee, Tae Keun Yoo:
Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography. BMC Medical Informatics Decis. Mak. 24(1): 25 (2024) - 2021
- [j9]Tae Keun Yoo, Joon Yul Choi, Hong Kyu Kim, Ik Hee Ryu, Jin Kuk Kim:
Adopting low-shot deep learning for the detection of conjunctival melanoma using ocular surface images. Comput. Methods Programs Biomed. 205: 106086 (2021) - [j8]Tae Keun Yoo, Joon Yul Choi, Hong Kyu Kim:
Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification. Medical Biol. Eng. Comput. 59(2): 401-415 (2021) - 2020
- [j7]Tae Keun Yoo, Joon Yul Choi, Hong Kyu Kim:
A generative adversarial network approach to predicting postoperative appearance after orbital decompression surgery for thyroid eye disease. Comput. Biol. Medicine 118: 103628 (2020) - [j6]Tae Keun Yoo, Joon Yul Choi, Younil Jang, Ein Oh, Ik Hee Ryu:
Toward automated severe pharyngitis detection with smartphone camera using deep learning networks. Comput. Biol. Medicine 125: 103980 (2020) - [j5]Tae Keun Yoo, Ik Hee Ryu, Jin Kuk Kim, In Sik Lee, Jung Sub Kim, Hong Kyu Kim, Joon Yul Choi:
Deep learning can generate traditional retinal fundus photographs using ultra-widefield images via generative adversarial networks. Comput. Methods Programs Biomed. 197: 105761 (2020) - [j4]Woojin Jung, Jaeyeon Yoon, Sooyeon Ji, Joon Yul Choi, Jae Myung Kim, Yoonho Nam, Eung-Yeop Kim, Jongho Lee:
Exploring linearity of deep neural network trained QSM: QSMnet+. NeuroImage 211: 116619 (2020)
2010 – 2019
- 2019
- [j3]Tae Keun Yoo, Joon Yul Choi, Jeong Gi Seo, Ramasubramanian Bhoopalan, Selvaperumal Sundaramoorthy, Deok Won Kim:
The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment. Medical Biol. Eng. Comput. 57(3): 677-687 (2019) - [j2]Hyeong-Geol Shin, Se-Hong Oh, Masaki Fukunaga, Yoonho Nam, Doo-Hee Lee, Woojin Jung, Minju Jo, Sooyeon Ji, Joon Yul Choi, Jongho Lee:
Advances in gradient echo myelin water imaging at 3T and 7T. NeuroImage 188: 835-844 (2019) - 2013
- [j1]Kyung-Ah Kim, Joon Yul Choi, Tae Keun Yoo, Sung Kean Kim, KilSoo Chung, Deok Won Kim:
Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques. Medical Biol. Eng. Comput. 51(9): 1059-1067 (2013) - 2012
- [c3]Min Kyung Kwon, Sung Kean Kim, Jeong Mo Koo, Joon Yul Choi, Deok Won Kim:
EHS subjects do not perceive RF EMF emitted from smart phones better than non-EHS subjects. EMBC 2012: 2190-2193 - [c2]Joon Yul Choi, Sung Kean Kim, Wan Hyung Lee, Tae Keun Yoo, Deok Won Kim:
A survival prediction model of rats in hemorrhagic shock using the random forest classifier. EMBC 2012: 5570-5573 - 2011
- [c1]Kyung Hwan Jang, Tae Keun Yoo, Joon Yul Choi, Ki Chang Nam, Jae Lim Choi, Min Kyung Kwon, Deok Won Kim:
Comparison of survival predictions for rats with hemorrhagic shocks using an artificial neural network and support vector machine. EMBC 2011: 91-94
Coauthor Index
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