home.md

-## Wiki for material and resources, Deep Learning for COVID X-Ray detection
+## Helmholtz AI COVIDNet X Initiative
+
+#### Transferable Deep Learning for explainable COVID X-Ray detection and diagnostics
 
 Authors: Mehdi Cherti (MC), Jenia Jitsev (JJ)
 (Helmholtz AI Local "Information", Juelich Supercomputing Center (JSC))
@@ -8,7 +10,7 @@ Further Contributors:
 #### Project Overview
 
 * [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)
-* For the project, **computing budget is available**, on JSC's JUSUF machine (up to 61 nodes with 1x V100)
+* For the project, **computing budget is available**, on JSC's JUSUF machine (up to 61 nodes with 1x V100 GPU)
   - [JUSUF Hardware Specs in detail](https://www.fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/JUSUF/Configuration/Configuration_node.html)
   - Budget limited until 31.10.2020 for initial project phase
   - Computational time project will be continued after successful initial phase
@@ -31,4 +33,4 @@ Following directions are currently envisaged, please feel free to add more:
 * Learning from Multi-Modal datasets (e.g, 2D X-Ray or 3D CT scans) (Collaborators: JSC, ...)
 * Neural Architecture Search for obtaining higly optimized architecture backbones (Collaborators: JSC, ...)
   - Transfer across different hardware architectures (e.g, mobile devices)
-* Data collection, preparation, maintenance (Collaborators: JSC (potential link to Juelich Datasets Initiative))
+* Data collection, preparation, maintenance (Collaborators: JSC (potential link to Juelich Datasets Initiative), HZDR, ...)