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Diagnostic Image Analysis Group

The Diagnostic Image Analysis Group is part of the Department of Radiology and Nuclear Medicine of Radboud University Medical Center. We develop computer algorithms to aid clinicians in the interpretation of medical images and thereby improve the diagnostic process.

The group has its roots in computer-aided detection of breast cancer in mammograms, and we have expanded to automated detection and diagnosis in breast MRI, ultrasound and tomosynthesis, chest radiographs and chest CT, prostate MRI, neuro-imaging and the analysis of retinal and digital pathology images.

It is our goal to have a significant impact on healthcare by bringing our technology to the clinic. We are therefore fully certified to develop, maintain, and distribute software for analysis of medical images in a quality controlled environment (MDD Annex II and ISO 13485). To date two products, ProCAD and CAD4TB, have been CE marked and are in active use in over ten countries.

On this site you find information about the history of the group and our collaborations, an overview of people in DIAG, current projects, publications and theses, contact information, and info for those interested to join our team.



Convolutional neural networks (CNNs) are network architectures that are becoming increasingly popular in medical image analysis, but are computationally expensive to train. Mark van Grinsven has developed a method to improve and speed up the CNN training. The method was applied to the automatic detection of hemorrhages in color fundus images. The Figures show an example case (left), the annotations made by a human expert (middle) and the output of the automatic system (right). His work has been published in the Special Issue on Deep Learning of Transactions on Medical Imaging.

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