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@@ -13,7 +13,7 @@ Further Contributors: Mehdi Cherti (MC) (Helmholtz AI HLST, JSC)
 * For model training and further experiments, **computing budget is available**,
   - **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."
+      - 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."
     - grant period 01.11.2020 - 31.10.2021
     - In the granting period we aim to lay grounds for different forms of advanced distributed deep transfer learning that is both high performant and efficient, offering low cost transfer to different domains and tasks, with focus of transfer on small size COVID-19 X-Ray lung images datasets after pre-training on publically available large scale natural image or medical image datasets. We will use supervised, unsupervised and NAS based transfer learning techniques to evaluate transfer quality.
   - on JSC's JUSUF machine (up to 61 nodes with 1x V100 GPU)
@@ -22,11 +22,11 @@ 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)
   - 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))
+  - 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 CheXPert, COVIDx, etc - 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 how strongly the provided images are **out-of-distribution**, signaling whether pre-trained model is likely not able to produce useful diagnostics on-fly for the given images and potential need for further re-calibration / fine-tuning on the new images before attempting diagnostics
   - **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**       
-  - Long-term vision is a **generic** system digesting different types of image modalities (not only X-Ray - eg. CT and 3D CT scans, including eventually entirely different modalities like ultrasonography, etc), **continually improving** generic model of image understanding (with strong focus on medical diagnostics and analysis of pathological signatures in this frame), allowing **fast transfer** to a specified domain of interest. So, if a new domain X comes up, triggered by an unknown novel pathogen Y causing a disease Z that can be diagnosed via medical imaging, the generic model, **pre-trained on millions of different images from distinct domains**, can be used to derive quickly an expert model for domain X. This should enable quick reaction in face of novel, yet unknown pathologies, where availability of diagnostics is initially impaired.
+  - **Long-term vision** is a generic system digesting **different types of image modalities** (not only X-Ray - eg. CT and 3D CT scans, including eventually entirely different modalities like ultrasonography, etc), **continually improving generic model** of image understanding (with strong focus on medical diagnostics and analysis of pathological signatures in this frame), allowing **fast transfer** to a specified domain of interest. So, if a new domain X comes up, triggered by an unknown novel pathogen Y causing a disease Z that can be diagnosed via medical imaging, the generic model, **pre-trained on millions of different images from distinct domains**, can be used to derive quickly an expert model for domain X. This should enable quick reaction in face of novel, yet unknown pathologies, where availability of diagnostics is initially impaired.
 * For collaborators: [Helmholtz AI COVIDNetX Initiative internal](https://gitlab.version.fz-juelich.de/MLDL_FZJ/juhaicu/jsc_internal/superhaicu/shared_space/playground/covid19/-/wikis/home)
 
 #### Directions and topics for collaborations