DIAG Research Software Engineering

The Research Software Engineering Team is part of the Diagnostic Image Analysis Group at Radboud University Medical Center in Nijmegen, The Netherlands.

We use industry best practices to develop software that enables researchers to rapidly develop novel machine learning algorithms that we can integrate into research workstations for clinical validation.

Automated tumor detection

Software

ASAP - Fluid whole-slide image viewer

ASAP (Automated Slide Analysis Platform) is a fast and fluid viewer for digitized multi-resolution histopathology images. ASAP offers several tools to make annotations in an intuitive way. Dots, rectangles, polygons are all supported. ASAP allows on-slide visualization of image analysis and machine learning results such as segmentation masks with customizable lookup-tables.

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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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Ophthalmology workstation

The goal of this project is to develop a software solution that assists researchers and specialists to view and annotate retinal images.

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SOL

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

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Members

Colin Jacobs

Colin Jacobs

Assistant Professor / Research Software Engineer

Haimasree Bhattacharya

Haimasree Bhattacharya

Research Software Engineer

Harm van Zeeland

Harm van Zeeland

Research Software Engineer

James Meakin

James Meakin

Lead Research Software Engineer

Joep Kamps

Joep Kamps

Data Manager

Miriam Groeneveld

Miriam Groeneveld

Research Software Engineer

Paul Konstantin Gerke

Paul Konstantin Gerke

Research Software Engineer

Rita Bylsma

Rita Bylsma

Research Software Engineer

Sil van de Leemput

Sil van de Leemput

Research Software Engineer

Sjoerd Kerkstra

Sjoerd Kerkstra

Research Software Engineer