Generative adversarial networks in dental imaging: a systematic review
S Yang, KD Kim, E Ariji, Y Kise - Oral Radiology, 2024 - Springer
Objectives This systematic review on generative adversarial network (GAN) architectures for
dental image analysis provides a comprehensive overview to readers regarding current …
dental image analysis provides a comprehensive overview to readers regarding current …
An unsupervised deep learning network model for artifact correction of cone-beam computed tomography images
W Zhang, H Ding, H Xu, MM Jin, G Huang - Biomedical Signal Processing …, 2024 - Elsevier
Unsupervised deep learning network model cycle-consistent generative adversarial network
(CycleGAN) is increasingly applied for artifact correction of cone-beam computed …
(CycleGAN) is increasingly applied for artifact correction of cone-beam computed …
Domain Adaptation based on Human Feedback for Enhancing Generative Model Denoising Abilities
HC Park, SH Kang - arXiv preprint arXiv:2308.00307, 2023 - arxiv.org
How can we apply human feedback into generative model? As answer of this question, in
this paper, we show the method applied on denoising problem and domain adaptation using …
this paper, we show the method applied on denoising problem and domain adaptation using …