Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 최장환 | * |
dc.date.accessioned | 2023-01-18T16:31:09Z | - |
dc.date.available | 2023-01-18T16:31:09Z | - |
dc.date.issued | 2022 | * |
dc.identifier.issn | 2169-3536 | * |
dc.identifier.other | OAK-32842 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/263810 | - |
dc.description.abstract | Deep neural networks have shown great improvements in low-dose computed tomography (CT) denoising. Early deep learning-based low-dose CT denoising algorithms were primarily based on supervised learning. However, supervised learning requires a large number of training samples, which is impractical in real-world scenarios. To address this problem, we propose a novel unsupervised domain adaptation approach for low-dose CT denoising. This proposed framework adapts the network pretrained with paired low- and normal-dose phantom images (source domain) to denoise unlabeled low-dose human CT images (target domain). Our framework modifies the action of the domain classifier, enabling the denoising network to be adapted to the target domain. Furthermore, we introduce a new backpropagation method, which we call domain-independent weighted backpropagation. By combining these techniques, we demonstrate that the denoising network can be properly trained without using clinical clean CT images. The experimental results showed that our method exhibited better performance in terms of both objective and perceptual image qualities when compared with current unsupervised denoising algorithms. Our proposed domain adaptation represents a first-use case in the context of CT denoising problems, with the possibility of extension to other image restoration tasks. | * |
dc.language | English | * |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | * |
dc.subject | Computed tomography | * |
dc.subject | Noise reduction | * |
dc.subject | Noise measurement | * |
dc.subject | Deep learning | * |
dc.subject | Unsupervised learning | * |
dc.subject | Training | * |
dc.subject | Machine learning algorithms | * |
dc.subject | Low-dose computed tomography (LDCT) denoising | * |
dc.subject | low-dose CT | * |
dc.subject | deep learning | * |
dc.subject | domain adaptation | * |
dc.subject | unsupervised learning | * |
dc.title | Unsupervised Domain Adaptation for Low-Dose Computed Tomography Denoising | * |
dc.type | Article | * |
dc.relation.volume | 10 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 126580 | * |
dc.relation.lastpage | 126592 | * |
dc.relation.journaltitle | IEEE ACCESS | * |
dc.identifier.doi | 10.1109/ACCESS.2022.3226510 | * |
dc.identifier.wosid | WOS:000896606700001 | * |
dc.author.google | Lee, Jaa-Yeon | * |
dc.author.google | Kim, Wonjin | * |
dc.author.google | Lee, Yebin | * |
dc.author.google | Lee, Ji-Yeon | * |
dc.author.google | Ko, Eunji | * |
dc.author.google | Choi, Jang-Hwan | * |
dc.contributor.scopusid | 최장환(55850525400) | * |
dc.date.modifydate | 20240318171633 | * |