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Wiki for material and resources, Deep Learning for COVID XRay detection
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Authors: Mehdi Cherti (MC), Jenia Jitsev (JJ)
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Authors: Mehdi Cherti (MC), Jenia Jitsev (JJ)
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(Helmholtz AI Local "Information", Juelich Supercomputing Center (JSC))
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- [Description of available models, codes and data](https://gitlab.version.fz-juelich.de/MLDL_FZJ/juhaicu/jsc_public/sharedspace/playground/covid_xray_deeplearning/wiki/-/blob/master/Description.md) |
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Further Contributors:
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#### Project Overview
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* [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)
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* For the project, **computing budget is available**, on JSC's JUSUF machine (up to 61 nodes with 1x V100)
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- [JUSUF Hardware Specs in detail](https://www.fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/JUSUF/Configuration/Configuration_node.html)
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- Budget limited until 31.10.2020 for initial project phase
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- Computational time project will be continued after successful initial phase
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- 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)
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- Collaborating partners will also gain access to common code and dataset repository
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* Projects aims are:
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- 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))
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- Long term vision is a generic system digesting different types of image modalities (not only X-Ray - eg. 3D CT, or entirely differrent modalities like PET, MRI, etc), continually improving generic model of image understanding (with strong focus on medical diagnostics and analysis 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 causing a disease 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. |
