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.
Anton Schreuder developed a risk model to determine whether participants in a lung cancer screening trial should return for a follow-up CT scan after one or two years after the baseline scan. With the gradual implementation of screening programs across the world, applying this model is expected to greatly decrease the number of unnecessary scans and false positive outcomes at the expense of delaying relatively few diagnoses. This paper was published online on March 30 Thorax.
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