166 citations · 767 across the 26 of their papers we have counts for
28 papers
A fully automated method for 3D individual tooth identification and segmentation in dental CBCT
Tae Jun Jang, Kang Cheol Kim, Hyun Cheol Cho +1
Accurate and automatic segmentation of three-dimensional (3D) individual teeth from cone-beam computerized tomography (CBCT) images is a challenging problem because of the difficul…
Automated 3D cephalometric landmark identification using computerized tomography
Hye Sun Yun, Chang Min Hyun, Seong Hyeon Baek +2
Identification of 3D cephalometric landmarks that serve as proxy to the shape of human skull is the fundamental step in cephalometric analysis. Since manual landmarking from 3D com…
Framelet Pooling Aided Deep Learning Network : The Method to Process High Dimensional Medical Data
Chang Min Hyun, Kang Cheol Kim, Hyun Cheol Cho +2
Machine learning-based analysis of medical images often faces several hurdles, such as the lack of training data, the curse of dimensionality problem, and the generalization issues…
Automatic detection and segmentation of lumbar vertebra from X-ray images for compression fracture evaluation
Kang Cheol Kim, Hyun Cheol Cho, Tae Jun Jang +2
For compression fracture detection and evaluation, an automatic X-ray image segmentation technique that combines deep-learning and level-set methods is proposed. Automatic segmenta…
Unpaired image denoising using a generative adversarial network in X-ray CT
Hyoung Suk Park, Jineon Baek, Sun Kyoung You +2
This paper proposes a deep learning-based denoising method for noisy low-dose computerized tomography (CT) images in the absence of paired training data. The proposed method uses a…
Improving learnability of neural networks: adding supplementary axes to disentangle data representation
Bukweon Kim, Sung Min Lee, Jin Keun Seo
Over-parameterized deep neural networks have proven to be able to learn an arbitrary dataset with 100 training accuracy. Because of a risk of overfitting and computational cost…