Generating and analyzing Synthetic data for

In this project, different deep generative models will be implemented to generate synthetic medical data for Student want to learn and implement state-of-the-art deep generative modles for a selected medical dataset.

Collecting and annotating medical domain data is time-consuming and costly. However, deep learning algorithms depend on high-quality datasets and annotations. In this regard, synthetic data shows promising directions to overcome this data deficiency problem in the medical domain. DeepSynthBody ( is the beginning of such an open-source project aiming at providing high-quality synthetic data.

The following sub-projects are available under this big project:

Sub-project 1: Generating medical data using Generative Adversarial Networks
Sub-project 2: Generating medical data using Diffusion Models
Sub-project 3: Conditional GANs and Diffusion models


Implement, train and evaluate different deep generative models for generating synthetic data for a selected medical dataset.

Learning outcome

  • Interdisciplinary research
  • Machine learning / AI (deep generative models)
  • Performing research with advanced deep learning algorithms


  • Hard-working
  • Motivated
  • Interested in learning (the rest can be learned during the thesis work)


  • Pål Halvorsen
  • Michael Riegler
  • Vajira Thambawita




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