RTC Deep Learning

We use machine learning, specifically deep learning, to analyze medical images and other medical data. We have set up a high-performance GPU cluster on which deep learning systems can be trained and deployed. We provide advise and develop algorithms and web-based image analysis and data analytics software.

Projects

Adipose and muscular tissue segmentation

For the department of Health Evidence we are developing an application for the segmentation and quantification of adipose and muscular tissue in non-contrast abdominal CT.

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AI for health

A collaboration between Radboud University and Radboudumc aimed at developing innovations based on artificial intelligence that solve clinical problems.

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AQUILA

The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.

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Brain MRI classification

In the context of the “Nationale Wetenschapsagenda” (NWA) roadmap for Value Creation through Responsible Access to and use of Big Data (VWdata) we are collecting data and setting up a challenge for the identification of healthy or abnormal MRI scans of the brain.

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ExaMode

The aim of ExaMode is to collect training data with limited human interaction for the processing of exascale volumes of healthcare data.

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ICH and PHE segmentation

For the CONTRAST consortium and Dutch ICH Surgery Trial (DIST) we are developing an application for the segmentation and quantification of intracerebral hemorrhage and peri-hematomal edema in non-contrast CT.

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Knee segmentation

For the Orthopedics department we are developing an application for the segmentation of the tibia, patella and fibula in non-contrast CT images of the legs.

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Artificial intelligence for lung cancer screening

We aim to improve the efficiency of lung cancer screening by using artificial intelligence.

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Muscle ultrasound classification

For the Neurophysiology department we have developed an algorithm for the identification of abnormal muscle tissue in ultrasound images of the tibialis anterior.

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Infrastructure & Software

CIRRUS

CIRRUS is the workstation platform for DIAG

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CIRRUS Lung Screening

This workstation is a highly optimized reading platform that allows fast and standardized reading of lung screening CT scans.

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Grand-challenge.org

The home of challenges in biomedical imaging.

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SOL

DIAG's deep learning cluster for training and applying automated image analysis tools.

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Members

Ajay Patel

Ajay Patel

Coordinator RTC Deep Learning

Harm van Zeeland

Harm van Zeeland

Research Software Engineer

James Meakin

James Meakin

Lead Research Software Engineer

Mike Overkamp

Mike Overkamp

Research Software Engineer

Miriam Groeneveld

Miriam Groeneveld

Research Software Engineer

Paul Konstantin Gerke

Paul Konstantin Gerke

Research Software Engineer

Rita Bylsma

Rita Bylsma

System Administrator

Sil van de Leemput

Sil van de Leemput

Research Software Engineer

Silvan Quax

Silvan Quax

Research Scientist, RTC Deep Learning