Healthcare is one of the key sectors of the global economy, especially in Europe. Any improvement in healthcare systems has a high impact on the welfare of the society. The use of technologies in health is clearly a strong path to more efficient healthcare, benefitting both individual people and the public budgets. European public health systems are generating large datasets of biomedical data in general, and images in particular, as most medical examinations use image-based processes. These datasets are in continuous growth and constitute a large unexploited knowledge database since most of its value comes from the interpretations of the experts. Nowadays, this process is generally performed manually and global knowledge sharing is complex.
In the context of automating and accelerating the analysis of the health data and processes, health scientific discovery and innovation are expected to quickly move forward under the so-called “fourth paradigm of science”, which relies on unifying the traditionally separated and heterogeneous high-performance computing and big data analytics environments. Under this paradigm, the DeepHealth project will provide HPC computing power at the service of biomedical applications; and apply Deep Learning (DL) techniques on large and complex biomedical datasets to support new and more efficient ways of diagnosis, monitoring and treatment of diseases.
The aim of DeepHealth is to offer a unified framework completely adapted to exploit underlaying heterogeneous HPC and Big Data architectures; and assembled with state-of-the-art techniques in Deep Learning and Computer Vision. In particular, DeepHealth framework is envisioned to tackle real needs of the health sector and facilitate the daily work of medical personnel and the expert users in terms of image processing and the use and training of predictive models without the need of combining numerous tools. To this end, the project will combine High-Performance Computing (HPC) infrastructures with Deep Learning (DL) and Artificial Intelligence (AI) techniques to support biomedical applications that require the analysis of large and complex biomedical datasets and thus, new and more efficient ways of diagnosis, monitoring and treatment of diseases. Moreover, two new libraries, the European Distributed Deep Learning Library (EDDLL) and the European Computer Vision Library (ECVL), will be developed and incorporated in the DeepHealth framework for manipulating and processing the images in a more efficient way and thus, for increasing the productivity of professionals working on biomedical images. The resulting enhanced diagnosis will significantly improve the health service provided to the society, making public health systems more efficient and profitable for everyone.