The increased accuracy and exhaustivity of modern Artificial Intelligence techniques in supporting the analysis of complex data, such as medical images, have exponentially increased real-world data collection for research purposes. This fact has led to the development of international repositories and high-performance computing solutions to deal with the computational demand for training models. However, other stages in the development of medical imaging biomarkers do not require such intensive computing resources, which has led to the convenience of integrating different computing backends tailored for the processing demands of the various stages of processing workflows. We present in this article a distributed and federated repository architecture for the development and application of medical image biomarkers that combines multiple cloud storages with cloud and HPC processing backends. The architecture has been deployed to serve the PRIMAGE (H2020 826494) project, aiming to collect and manage data from paediatric cancer. The repository seamlessly integrates distributed storage backends, an elastic Kubernetes cluster on a cloud on-premises and a supercomputer. Processing jobs are handled through a single control platform, synchronising data on demand. The article shows the specification of the different types of applications and a validation through a use case that make use of most of the features of the platform.

This paper introduces a platform to support serverless computing for scalable event-driven data processing that features a multi-level elasticity approach combined with virtualization of GPUs. The platform supports the execution of applications based on Docker containers in response to file uploads to a data storage in order to perform the data processing in parallel. This is managed by an elastic Kubernetes cluster whose size automatically grows and shrinks depending on the number of files to be processed. To accelerate the processing time of each file, several approaches involving virtualized access to GPUs, either locally or remote, have been evaluated. A use case that involves the inference based on deep learning techniques on transthoracic echocardiography imaging has been carried out to assess the benefits and limitations of the platform. The results indicate that the combination of serverless computing and GPU virtualization introduce an efficient and cost-effective event-driven accelerated computing approach that can be applied for a wide variety of scientific applications.