Diagnostic Image Analysis Group

The Diagnostic Image Analysis Group is part of the Departments of Radiology and Nuclear Medicine, Pathology, and Ophthalmology 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. The technology we primarily use is deep learning.

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).

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.


December, 2018


As a result of the second ‘Onderzoek & Implementatie’ program call this year, 3 KWF grants were awarded this month to Radboud Imaging Research group-members. As part of a consortium led by Mireille Broeders, Nico Karssemeijer, Ritse Mann and Jonas Teuwen will investigate the correlation of mammographic image features with pathological subtypes and prognosis. John Hermans and Henkjan Huisman will work together with Lodewijk Brosens on defining a vascular phenotype of pancreatic cancer. Last, Francesco Ciompi will work as project leader together with his team on the PROACTING project. Francesco, Jeroen van der Laak, Jelle Wesseling (NCI) and Esther Lips (NCI) aim in this project to predict the response to neo-adjuvant treatment of breast cancer patients. All project make use of deep learning techniques; depicted above is a mammographic image in which the tumor is delineated by one of the networks that shall be used for the project of Mireille Broeders et al.

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