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@@ -18,6 +18,8 @@ Further Contributors: Mehdi Cherti (MC) (Helmholtz AI HLST, JSC)
   - For collaboration and access to computing resources, please contact Jenia Jitsev (j.jitsev@fz-juelich.de), Mehdi Cherti (m.cherti@fz-juelich.de), or Alex Strube (a.strube@fz-juelich.de)
   - **UPDATE**: 30.10.2020 - COVIDNetX computational time application granted for JUWELS Booster (ca. **3600 GPUs** !)
     - Grant title: *"Large-Scale Advanced Deep Transfer Learning for Fast, Robust and Affordable COVID-19 X-Ray Diagnostics"*
+      - Abstract: "X-Ray imaging based diagnostics offers an affordable, widely available and easily deployable alternative for screening of COVID-19 disease caused by the new coronavirus, the SARS-CoV-2. However, large-scale screening of substantial numbers of images under time pressure may cause human based errors and especially in remote locations without enough skilled personal and specialized physicians, both reliability of diagnostics and its rapid execution can be severely affected, corroborating the results. In this project, we aim on establishing advanced deep transfer learning approaches to enable robust and fast X-Ray based diagnostic tools independent of location and local resources. **Large-scale generic models pre-trained in HPC**, **quickly adaptable to local demands via transfer learning**,  can be deployed as compact, robust, fast and low cost diagnostic tools, assisting available or even replacing missing medical personal, thus allowing screening in yet unprecedented speed and quality."
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   - Collaborating partners will also gain access to common code and dataset repository
 * Initiative's aims are:
   - short-term: provide **strong baseline** for pre-training and **transfer learning** for COVID X-Ray diagnostics using large-scale datasets of images from different domains (using both generic datasets like ImageNet and medical imaging datasets like COVIDx - see [Description of available models, codes and datasets](https://gitlab.version.fz-juelich.de/MLDL_FZJ/juhaicu/jsc_public/sharedspace/playground/covid_xray_deeplearning/wiki/-/blob/master/Description.md))