Deep learning in medical image analysis (DLMedIA)

Imaging is a cornerstone of medicine. The number and volume of radiology exams is growing rapidly thereby tremendously increasing the workload of radiologists. Deep learning methods can potentially extract more information from images, more reliably, more accurately, and most notably fully automatically.
The goal of the DLMedIA programme is two fold: 1) Develop a technological software platform for application development and; 2) build specific solutions for personalized and precision medicine. Through four different projects and in collaboration with four research centers and two industrial partners (Philips Healthcare, Pie Medical Imaging) , in our group we focus on early detection and prevention cardiovascular disease.

Research projects

High dimensional data: To develop deep learning techniques for quantitative analysis of 4- and 5-dimensional medical images.

Deep generative models: To incorporate expert knowledge in the form of generative models in deep learning in order to learn more efficiently from less data.

Deep transfer learning: To develop deep transfer learning techniques to effectively analyze heterogeneous medical imaging data with variation in scanners, scan protocols, and patient populations.

Dynamic deep learning: To develop dynamic learning strategies for deep learning systems in clinical environments.

Research partners

Prof. dr. Bram van Ginneken, Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen
Dr. Clarisa Sanchez, Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen
Prof. dr. Max Welling, Amsterdam Machine Learning Lab, Department of Science University of Amsterdam (UvA)
Dr. Marleen de Bruijne, Biomedical Imaging Group Rotterdam, Erasmus Medical Center Rotterdam
Prof. dr. Josien Pluim Medical Image Analysis, Eindhoven University of Technology