Choi, Kihwan Vania, Malinda Kim, Sungwon 2024-01-19T10:37:23Z 2024-01-19T10:37:23Z 2022-03-07 2019 1557-170X https://pubs.kist.re.kr/handle/201004/114309 In the absence of duplicate high-dose CT data, it is challenging to restore high-quality images based on deep learning with only low-dose CT (LDCT) data. When different reconstruction algorithms and settings are adopted to prepare high-quality images, LDCT datasets for deep learning can be unpaired. To address this problem, we propose hierarchical deep generative adversarial networks (HD-GANs) for semi-supervised learning with the unpaired datasets. We first cluster each patient's CT images into multiple categories, and then collect the images in the same categories across different patients to build an imageset for denoising. Each imageset is fed into a generative adversarial network that consists of a denoising network and a following classification network. The denoising network efficiently reuses feature maps from the lower layers for end-to-end learning with full-size images. The classifier is trained to distinguish between the denoised images and the high-quality images. Evaluated with a clinical LDCT dataset, the proposed semi-supervised learning approach efficiently reduces the noise level of LDCT images without loss of information, thereby addressing the major shortcomings of IR such as computation time and anatomical inaccuracy. English IEEE Semi-Supervised Learning for Low-Dose CT Image Restoration with Hierarchical Deep Generative Adversarial Network (HD-GAN) Conference 1 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.2683 - 2686 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2683 2686 US Berlin, GERMANY 2019-07-23 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) 000557295303026 2-s2.0-85077880349 Proceedings Paper