The rise of big data and artificial intelligence techniques such as deep learning has lead to an exponential increase in stored data in various fields, including medical imaging, genetics and financial trading. Sharing these increasing amounts of data for research is challenging, as privacy risks increase with the increased size of data. Physically moving very large datasets to researchers is inconvenient, as download or sending physical hard disks are not optimal. Research on sensitive data is often not possible, as sharing is not legal. The popularity of container-based technologies such as Docker has revolutionized the way applications are deployed, due to their self-sufficient, light-weight and portable nature. In this paper, we propose a novel distributed platform using containers for simple execution and evaluation of research applications on the data owner's infrastructure, bringing the algorithms to the data. This approach avoids the cumbersome transfer of large datasets and can help circumventing problems linked to non-shareable data by providing a sandboxed execution environment with read-only access to the data. At no point the data leave the data owner's site, giving researchers access to their evaluation results, only, and not the data themselves. The presented proof-of-concept confirms the feasibility of a distributed container-based evaluation platform for large and/or sensitive data. This has several advantages, including execution of code instead of submission of result files and availability of otherwise inaccessible data. The container architecture allows for minimal computational overhead, no software dependency management on the infrastructure, distributed runtime environment and isolation of processes from the underlying host system. A version addressing various identified architectural and security-related challenges has the potential to be deployed in a production setting and therefore allows researchers to gain insights from previously inaccessible data. One goal is to target hospitals with increasingly strong local infrastructure for storage and computation, needed for artificial intelligence based decision support (genetics and imaging).

Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non-manually-annotated medical images are available to the research community.

In this paper, we propose mobile access to peer-reviewed medical information based on textual search and content-based visual image retrieval. Web-based interfaces designed for limited screen space were developed to query via web services a medical information retrieval engine optimizing the amount of data to be transferred in wireless form. Visual and textual retrieval engines with state-of-the-art performance were integrated. Results obtained show a good usability of the software. Future use in clinical environments has the potential of increasing quality of patient care through bedside access to the medical literature in context.