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.
Manual counting of mitotic tumor cells in tissue sections constitutes one of the strongest prognostic markers for breast cancer. This procedure, however, is time-consuming and error-prone. David Tellez developed a method to automatically detect mitotic figures in H&E stained breast cancer tissue sections based on convolutional neural networks (CNNs). The image shows a selection of patches identified by the CNN as containing a mitotic figure. From 181 detections, 128 patches were classified as true positives by a resident pathologist, resulting in a precision score of 0.707. The work was published in IEEE Transactions on Medical Imaging.
More Research Highlights.