Showing results 1 to 28 of 28
A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction Kang, Eunhee; Min, Junhong; Ye, Jong Chul, MEDICAL PHYSICS, v.44, no.10, pp.e360 - e375, 2017-10 |
A MATHEMATICAL FRAMEWORK FOR DEEP LEARNING IN ELASTIC SOURCE IMAGING Yoo, Jaejun; Wahab, Abdul; Ye, Jong Chul, SIAM JOURNAL ON APPLIED MATHEMATICS, v.78, no.5, pp.2791 - 2818, 2018-11 |
A performance comparison of convolutional neural network-based image denoising methods: The effect of loss functions on low-dose CT images Kim, Byeongjoon; Han, Minah; Shim, Hyunjung; Baek, Jongduk, MEDICAL PHYSICS, v.46, no.9, pp.3906 - 3923, 2019-09 |
AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising Gu, Jawook; Ye, Jong Chul, IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, v.7, pp.73 - 85, 2021-01 |
Cooperative Multi-Robot Task Allocation with Reinforcement Learning Park, Bumjin; Kang, Cheongwoong; Choi, Jaesik, APPLIED SCIENCES-BASEL, v.12, no.1, 2022-01 |
Cycle-consistent adversarial denoising network for multiphase coronary CT angiography Kang, Eunhee; Koo, Hyun Jung; Yang, Dong Hyun; Seo, Joon Bum; Ye, Jong Chul, MEDICAL PHYSICS, v.46, no.2, pp.550 - 562, 2019-02 |
Cycle-Free CycleGAN Using Invertible Generator for Unsupervised Low-Dose CT Denoising Kwon, Taesung; Ye, Jong Chul, IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, v.7, pp.1354 - 1368, 2021 |
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems Ye, Jong Chul; Han, Yoseob; Cha, Eunju, SIAM JOURNAL ON IMAGING SCIENCES, v.11, no.2, pp.991 - 1048, 2018-07 |
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets Oh, Yujin; Park, Sangjoon; Ye, Jong Chul, IEEE TRANSACTIONS ON MEDICAL IMAGING, v.39, no.8, pp.2688 - 2700, 2020-08 |
Deep learning with domain adaptation for accelerated projection-reconstruction MR Han, Yo Seob; Yoo, Jaejun; Kim, Hak Hee; Shin, Hee Jung; Sung, Kyunghyun; Ye, Jong Chul, MAGNETIC RESONANCE IN MEDICINE, v.80, no.3, pp.1189 - 1205, 2018-09 |
Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks Lee, Dongwook; Yoo, Jaejun; Tak, Sungho; Ye, Jong Chul, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, v.65, no.9, pp.1985 - 1995, 2018-09 |
DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging Using Deep Learning Ryu, DongHun; Ryu, Dongmin; Baek, YoonSeok; Cho, Hyungjoo; Kim, Geon; Kim, Young Seo; Lee, Yongki; et al, IEEE TRANSACTIONS ON MEDICAL IMAGING, v.40, no.5, pp.1508 - 1518, 2021-05 |
Efficient B-Mode Ultrasound Image Reconstruction From Sub-Sampled RF Data Using Deep Learning Yoon, Yeo Hun; Khan, Shujaat; Huh, Jaeyoung; Ye, Jong Chul, IEEE TRANSACTIONS ON MEDICAL IMAGING, v.38, no.2, pp.325 - 336, 2019-02 |
Enhanced Diagnosis of Plaque Erosion by Deep Learning in Patients With Acute Coronary Syndromes Park, Sangjoon; Araki, Makoto; Nakajima, Akihiro; Lee, Hang; Fuster, Valentin; Ye, Jong Chul; Jang, Ik-Kyung, JACC-CARDIOVASCULAR INTERVENTIONS, v.15, no.20, pp.2020 - 2031, 2022-10 |
Explainable Artificial Intelligence Approach to Identify the Origin of Phonon-Assisted Emission in WSe2 Monolayer Yoo, Jaekak; Cho, Youngwoo; Jeong, Byeonggeun; Choi, Soo Ho; Kim, Ki Kang; Lim, Seong Chu; Lee, Seung Mi; et al, ADVANCED INTELLIGENT SYSTEMS, v.5, no.7, 2023-07 |
HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks Park, Heungseok; Nam, Yoonsoo; Kim, Jihoon; Choo, Jaegul, IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, v.27, no.2, pp.1407 - 1416, 2021-02 |
Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning Ravishankar, Saiprasad; Ye, Jong Chul; Fessler, Jeffrey A., PROCEEDINGS OF THE IEEE, v.108, no.1, pp.86 - 109, 2020-01 |
Learning Polymorphic Neural ODEs with Time-evolving Mixture Yoon, Tehrim; Shin, Sumin; Yang, Eunho, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v.45, no.1, pp.712 - 721, 2023-01 |
Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network Moinuddin, Muhammad; Khan, Shujaat; Alsaggaf, Abdulrahman U.; Abdulaal, Mohammed Jamal; Al-Saggaf, Ubaid M.; Ye, Jong Chul, FRONTIERS IN PHYSIOLOGY, v.13, 2022-11 |
Missing Cone Artifact Removal in ODT Using Unsupervised Deep Learning in the Projection Domain Chung, Hyungjin; Huh, Jaeyoung; Kim, Geon; Park, Yong Keun; Ye, Jong Chul, IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, v.7, pp.747 - 758, 2021-07 |
Multi-domain CT translation by a routable translation network Kim, Hyunjong; Oh, Gyutaek; Seo, Joon Beom; Hwang, Hye Jeon; Lee, Sang Min; Yun, Jihye; Ye, Jong Chul, PHYSICS IN MEDICINE AND BIOLOGY, v.67, no.21, 2022-11 |
One network to solve all ROIs: Deep learning CT for any ROI using differentiated backprojection Han, Yoseob; Ye, Jong Chul, MEDICAL PHYSICS, v.46, no.12, pp.E855 - E872, 2019-12 |
Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review Xiao, Cao; Choi, Edward; Sun, Jimeng, JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, v.25, no.10, pp.1419 - 1428, 2018-10 |
Scheduling PID Attitude and Position Control Frequencies for Time-Optimal Quadrotor Waypoint Tracking under Unknown External Disturbances Kang, Cheongwoong; Park, Bumjin; Choi, Jaesik, SENSORS, v.22, no.1, 2022-01 |
Unpaired MR Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation Oh, Gyutaek; Lee, Jeong Eun; Ye, Jong Chul, IEEE TRANSACTIONS ON MEDICAL IMAGING, v.40, no.11, pp.3125 - 3139, 2021-11 |
Unpaired Training of Deep Learning tMRA for Flexible Spatio-Temporal Resolution Cha, Eunju; Chung, Hyungjin; Kim, Eung Yeop; Ye, Jong Chul, IEEE TRANSACTIONS ON MEDICAL IMAGING, v.40, no.1, pp.166 - 179, 2021-01 |
Using recurrent neural network models for early detection of heart failure onset Choi, Edward; Schuetz, Andy; Stewart, Walter F.; Sun, Jimeng, JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, v.24, no.2, pp.361 - 370, 2017-03 |
시계열 심층학습 모델의 은닉 노드에 대한 시각화 조소희; 최재식, 정보과학회논문지, v.47, no.5, pp.445 - 453, 2020-05 |
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