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.
Deep learning has slowly pervaded every aspect of medical imaging. Recently, DIAG published an extensive review, titled ‘A Survey on Deep Learning in Medical Image Analysis’ in the journal Medical Image Analysis. It covers a significant part of the medical imaging field, ranging from radiology to pathology and ophthalmology. The review is subdivided into four main parts. The first part briefly introduces some general concepts in deep learning and some basic neural network architectures. Subsequently, we discuss several novel and interesting applications of deep learning with respect to specific tasks in medical image analysis, for example detection, segmentation and image generation and enhancement. The third part focuses on different application areas. We give a thorough overview of all papers published for every specific area like brain imaging, digital pathology or color fundus images. Last, we provide some insight on current challenges and opportunities for deep learning in medical image analysis and shed some light on the potential application of novel architectures like generative adversarial networks and variational auto-encoders. We hope the paper can function as a primer for both medical image analysis researchers interested in applying deep learning algorithms to their work and computer scientists who want to ventures into medical imaging. You can download the survey from the following sites: arXiv and MEDIA
More Research Highlights.