The main objective of the DeepHealth project is the development of both a European Deep Learning Library and a European Image Processing Library. These new data-driven AI libraries will make intensive use of hybrid HPC + Big Data architectures to process data by parallelising algorithms. The integration of both libraries into software platforms will considerably reduce the time for training Deep Learning based models and increase the productivity of IT experts (ML practitioners and data scientists) working in the health sector. IT experts giving support to doctors and other medical personnel usually face with the problem of image manipulation (i.e. transformations, segmentation, labelling, and extraction of regions of interest) where they need to use a set of different libraries and toolkits from different developers to define a pipeline of operations on images. Installing and configuring different libraries and toolkits is repetitive hard work. The DeepHealth project focuses on facilitating expert daily work by integrating all the necessary functionalities into a toolkit including the two libraries and a front-end for using them. The toolkit (one of the outcomes of this project) will facilitate the definition of pipelines of operations on images and the testing of distinct DNN topologies.
Tree Technology will be in charge of defining and implementing the Big Data architecture to adapt the defined libraries to the cloud and big data environments.
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825111.
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