Diagnostic pathology involves microscopic evaluation of human tissues. Increasingly, microscopic images are digitized to support the diagnostic workflow. This rapidly growing field of ‘digital pathology’ also yields ample opportunities for development of computer-aided diagnosis (CAD) algorithms. State-of-the-art deep learning methods have recently been proven capable of supporting the diagnostic work of pathologists. We have now reached the point where such algorithms can be implemented in a routine clinical setting. In a public-private collaboration we will start development of deep learning algorithms that will form the basis for future commercial products. For this project we are seeking a postdoctoral researcher.
You should be a creative and enthusiastic researcher with a PhD degree in a relevant field, such as medical image analysis, computer vision, or machine learning. You should have a clear interest to develop image analysis algorithms and an affinity with medical topics. Good communication skills and expertise in software development, preferably in Pytrhon, are essential.
The Computational Pathology Group (CPG) is a research group of the department of Pathology of the Radboud university medical center. CPG works closely together with The Diagnostic Image Analysis Group (DIAG) of the Department of Radiology and Nuclear Medicine. We develop, validate and deploy novel medical image analysis methods, usually based on machine learning technology and focusing on computer-aided diagnosis (CAD). Application areas include diagnostics and prognostics of breast, prostate and colon cancer. Our group is among the international front runners in the field, witnessed for instance by the highly successful CAMELYON16 and CAMELYON17 grand challenges which we organized. We closely collaborate with clinicians and industry.