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.
Depicted above is part of the poster presented by Hans Pinckaers at the first edition of MIDL 2018 held on the 4th-6th of July in Amsterdam, The Netherlands. The graphics nicely show his work on how to train a normal CNN with 8192x8192 input sizes and a single label on only one GPU. For code check out github.
Hans' poster was one of the 61 posters presented at the conference, next to an additional 21 oral presentations. Applicants representing 25 different countries submitted 122 papers and 99 abstracts, of which 41% and 35% were accepted respectively. The organizers thank everyone who attended MIDL 2018, and hope to see many of you at MIDL 2019 in London.
More Research Highlights.