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.


July, 2019

MIDL 19 crop.jpeg

During the 2nd edition of MIDL, Thomas de Bel won the Best Poster Award for his presentation on stain-transforming cycle-consistent generative adversarial networks (cycleGAN) for improved segmentation of renal histopathology.

Color variations in digital histopathological slides due to differences in tissue processing or scanning techniques can negatively affect the performance of deep learning applications. Thomas de Bel et al applied cycleGAN for stain transformation between two centers and have adapted the orginical cycleGAN architecture for improved training stability and performance, generating high quality artifically stained images. The authors trained two segmentation networks for the analysis of renal tissue using single center data; one with tranformed images, and one without. Stain transformation proved to be beneficial for the segmentation performance on data sets from both centers, raising the Dice-coefficients from 0.36 to 0.85 and from 0.45 to 0.73. Read more about this work in the Proceeding of Machine Learning Research.

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