Developing robust medical AI application requires large and diverse datasets for training. Medical data, however, is often limited because it contains sensitive human data that poses a risk to data privacy. Available medical datasets were often collected under defined settings (e.g. among symptomatic people seeking care), that may be different from future application settings, which can lead to failures during deployment. Datasets to test the performance of medical AI algorithms within a variety of possible test cases are lacking, but needed to advance the development of robust medical AI.
Project Aims
The aims of this project are to synthesize realistic medical images, which can be used to train and test medical AI algorithms that are applicable in clinical practice. The specific aims are as followed:
- Develop methods to generate medical images and apply them train and test the performance of medical algorithms.
- Provide synthetic images to the research field
- Integrate generative methods in a software package and distribute it though our SME-project partners
Funding
This research is supported through the German Federal Ministry of Education and Research (BMBF), Grant No. 01/S21069A.
