Radboudumc Technology Center Deep learning

What is deep learning?

Deep learning is an AI technique that is able to learn complex patterns in large data sets. It uses algorithms that mimic the workings of the human brain, so-called ‘neural networks’. Deep learning is especially suited to recognize patterns in (medical) image data, for example to help in diagnostics. But also in other medical areas where large amounts of data are available, such as genetics, temporal monitoring or large texts (patient records), deep learning can greatly enhance your research or clinical practices. For examples of deep learning applications in healthcare visit: https://rtc.diagnijmegen.nl/.

AI for Health

Our RTC coordinates the Radboud AI for Health initiative, where Radboud University and Radboudumc collaborate to create AI innovations in healthcare. We offer courses for professionals, MSc student projects and PhD projects. For more information on Radboud AI for Health, please visit: https://www.ai-for-health.nl/.

Grand Challenge

We develop and maintain grand-challenge.org, a website for hosting challenges in biomedical imaging, annotating data and making deep learning models accessible to clinicians. 

By promoting researchers to share their data and deep learning models we contribute to reproducible science that leads to more valuable healthcare innovations.

Challenges in biomedical image analysis have proven successful in driving innovation in creating algorithms that gain researchers new insights and assist clinicians in their decision process.

The platform promotes clinical validation of algorithms by allowing clinicians to execute algorithms on their own data via a web interface.

Cirrus Core

For many applications, visualization of your algorithms results is crucial. For this purpose we developed Cirrus Core, a visualization software based on MevisLab that allows for 2D and 3D viewing of medical image data.

The results of your algorithms can easily be added as an overlay, e.g. segmentations of anatomical structures, providing a visual aid for researchers and clinicians to interpret their algorithms.

SOL

RTC Deep Learning has set up a high-performance deep learning infrastructure called SOL. SOL offers researches access to a centralized pool of high-power general purpose graphic processing units (GPUs). It is used to create state-of-the-art tools for detecting and classifying illnesses and quantify extent and identify possible treatment options for diseases.

The GPU cluster consists of 40 compute nodes that give access to a pool of 100 GPUs (mainly NVIDIA GTX1080Ti and RTX2080Ti cards), a dedicated 500 TB storage server for serving data to deep learning compute nodes using 20 GBit networking, and an advanced monitoring solution to monitor the activity and health of the cluster.