Generative methods for image synthesis with applications to medical imaging
| dc.contributor.advisor | Ebrahimi, Mehran | |
| dc.contributor.advisor | Davoudi, Heidar(Koroush) | |
| dc.contributor.author | Abdollahi, Melika | |
| dc.date.accessioned | 2024-06-11T16:36:45Z | |
| dc.date.available | 2024-06-11T16:36:45Z | |
| dc.date.issued | 2024-04-01 | |
| dc.degree.discipline | Computer Science | |
| dc.degree.level | Master of Science (MSc) | |
| dc.description.abstract | There has been significant advancement in the field of medical image synthesis for diagnostic and analytical improvements. Generative methods for image synthesis have been an active area of research in recent years. In this thesis, we explore the use of a hybrid model of generative adversarial networks (GANs) and transformer networks for image synthesis. We propose a method that combines GANs with transformer networks to address the translation and super-resolution of medical images. We also present a model for inpainting of images. Finally, we introduce a re-parameterization model that translates one image modality to another modality with paired parameters. The proposed methods are evaluated qualitatively and quantitatively. Our results show that our proposed models are feasible and applicable for super-resolution, translation, inpainting and re-parameterization. | |
| dc.description.sponsorship | University of Ontario Institute of Technology | |
| dc.identifier.uri | https://ontariotechu.scholaris.ca/handle/10155/1766 | |
| dc.language.iso | en | |
| dc.subject.other | Generative adversarial network | |
| dc.subject.other | Transformer network | |
| dc.subject.other | Image synthesis | |
| dc.title | Generative methods for image synthesis with applications to medical imaging | |
| dc.type | Thesis |