MA: 3D GAN for conditional medical image synthesis and cross-modality translation

Advisor: Prof. Bjoern Menze
Supervision by: Suprosanna Shit

Abstract

GAN in 3D, especially in medical imaging application is challenging in many aspects, mainly due to the 'curse of dimensionality' and limited available data set. The goal of this project is to develop an optimum strategy to scale GAN in 3D that generalizes well for conditional medical image synthesis. The student will be provided with all-round support including good research environment, sufficient computational resources and active guidance to make the thesis successful.

Tasks

Develop state-of-the-art 3D GAN in challenging benchmark datasets for a conditional generation.

Requirements

• Sound knowledge about deep learning and generative modeling.
• Good programming skills in PyTorch^?.

Contact

Suprosanna Shit

Bjoern Menze