Synthetic data generation of CoVID-19 CT/X-rays images for enabling fast triage of healthy vs. unhealthy patients
At a glance
- Project leader : Dr. Javier Montoya
- Project team : Mohammadreza Amirian
- Project budget : CHF 19'740
- Project status : completed
- Funding partner : Internal (ZHAW digital / Digital Futures Fund)
The automatic analysis of X-ray/CT images through artificial intelligence models can be useful to automate the clinical scanning procedure. Nonetheless, the limited access to real COVID patient data leads to the need of synthesizing image samples. The goal of this project is to use existing CT/X-ray image datasets to develop a medical image synthesis model that can be used for further recognising healthy vs. unhealthy patients. The proposed models can be easily scaled up and transferred to hospitals and research institutes having a long-term impact on applied AI.
Amirian, Mohammadreza; Montoya, Javier; Gruss, Jonathan; Stebler, Yves D.; Bozkir, Ahmet Selman; Calandri, Marco; Schwenker, Friedhelm; Stadelmann, Thilo,
Proceedings of CISP-BMEI’21.
14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China, 23-25 October 2021.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-23318