Deakin University
Browse
- No file added yet -

Medical image synthesis using generative adversarial networks: towards photo-realistic image synthesis

Download (51.1 MB)
thesis
posted on 2018-09-01, 00:00 authored by Mohamed Hassan Attia
This proposed work addresses the photo-realism for synthetic images. We introduced a modified generative adversarial network: StencilGAN. It is a perceptually-aware generative adversarial network that synthesizes images based on overlaid labelled masks. This technique can be a prominent solution for the scarcity of the resources in the healthcare sector.

History

Pagination

193 p.

Open access

  • Yes

Material type

thesis

Resource type

thesis

Language

eng

Degree type

Research doctorate

Degree name

Ph.D.

Copyright notice

The author

Editor/Contributor(s)

M Hossny Mohammed

Faculty

Deputy Vice-Chancellor Research Group

School

Institute for Intelligent Systems Research and Innovation

Usage metrics

    Theses

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC