Generative methods for image synthesis with applications to medical imaging

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.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Abdollahi, Melika

Advisors dc:contributor.advisor

• Ebrahimi, Mehran
• Davoudi, Heidar(Koroush)

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1766

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1766