Vacancy: Deep learning for Prostate MRI Diagnosis



One million men receive a prostate cancer diagnosis, and three hundred thousand die from prostate cancer each year worldwide. Magnetic Resonance Imaging (MRI) represents a major breakthrough by accurately detecting clinically significant prostate cancers at an early, potentially more curable stage. The same MRI can also be used for better treatment targeting and for avoiding unnecessary systematic biopsies. As a result, the demand for Prostate MRI is rapidly increasing. Unfortunately, reading these multiparametric MRI (mpMRI) exams is difficult and requires substantial expertise. Computers can potentially extract more information from mpMRI, more reliably, and more accurately than human readers. Artificial Intelligence and more specifically Deep Learning is becoming indispensable in helping improve mpMRI diagnostic performance.

This project aims to research deep learning computer-aided diagnostic systems that will demonstrably help clinicians to get the best possible prostate cancer diagnosis from mpMRI. The research includes automatic multi-object segmentation, quantitative imaging biomarkers, and multivariate analysis using a massive MRI database. We closely work with clinicians in all projects and are collecting extensive data sets of expert annotations. State-of-the-art deep learning models will be continuously evaluated and implemented in a clinical prototype for validation and feedback. Results will be presented at scientific meetings and published in journals.

Radboudumc is a clinical expert on prostate MRI and technical expert in the field of prostate AI technology for over 20 years and an early adopter of deep learning in medical imaging. Deep learning is currently the most active research area within machine learning and computer vision, and medical image analysis. DIAG currently has 30 deep learning researchers focused on various medical image analysis topics. A team of scientific programmers is supporting our deep learning research, maintaining a large cluster of computers equipped with high-end GPUs for large-scale experimentation.


You are a creative and ambitious researcher with an MSc/Ph.D. degree in Computer Science, Data Science, Physics, Engineering or Biomedical Sciences or similar, with a clear interest in machine learning, deep learning, medical image analysis. Good communication skills and expertise in software development, preferably in Python/C++, are essential. Experience with deep learning should be evident from the (online) courses you've followed, your publications, GitHub account, etc.

Terms of employment

Positions can be filled by either a Ph.D. student.

  • Ph.D. positions have the standard salary and secondary conditions for Ph.D. students in the Netherlands. Your performance will be evaluated after 1 year. If the evaluation is positive, the contract will be extended by 3 years. The research should result in a Ph.D. thesis. See also this page for some general information about doing a Ph.D. in our group.


The Diagnostic Image Analysis Group (DIAG) is a research division of the Department of Radiology and Nuclear Medicine of the Radboud University Medical Center Nijmegen. Nijmegen is the oldest Dutch city with a rich history and one of the liveliest city centers in the Netherlands. Radboud University has over 17,000 students. Radboud UMC is a leading academic center for medical science, education, and healthcare with over 8,500 staff and 3,000 students.

The focus of DIAG is the development and validation of novel methods in a broad range of medical imaging applications. Research topics include image analysis, image segmentation, machine learning, and the design of decision support systems. Application areas include neuro, breast, prostate, lung and retina imaging and digital pathology. Key to the success of the group is close cooperation with clinicians. Currently, the group consists of around 40 researchers. You may read our deep learning survey paper to get an idea of our work in this field.

We offer excellent research facilities with large data storage facilities, a cluster of 40 high-end GPUs which can be scaled dynamically to include cloud servers, and support from a team of research software engineers, data analysts, and radiologists.


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