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 ## Helmholtz AI COVIDNet X Initiative
 
-#### Transferable Deep Learning for explainable COVID X-Ray detection and diagnostics
+### Transferable Deep Learning for explainable COVID X-Ray detection and diagnostics
 
 Author: Jenia Jitsev (JJ)
 (Helmholtz AI Local "Information", Juelich Supercomputing Center (JSC))
 
-Further Contributors: Mehdi Cherti (MC) (Helmholtz AI HLST, JSC) 
+Further Contributors: Mehdi Cherti (MC) (Helmholtz AI HLST, JSC)
 
-#### Project Overview
+#### Initiative 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 experiments and model training, **computing budget is available**, on JSC's JUSUF machine (up to 61 nodes with 1x V100 GPU)
+* For model training and further experiments, **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
   - 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)
   - Collaborating partners will also gain access to common code and dataset repository
-* Projects aims are:
+* 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))
   - **indicate** for the public users (medical doctors, etc) how **certain / uncertain** the performed classification is, given images provided by the users
   - **indicate** for the users on which basis the classification was made. e.g by highlighting regions of the input X-Ray image by a heat map or visualizing receptive fields of responsible activations across layers showing which **image regions** or **intermediate features** are **essential for diagnostics decision**