The Gleason score suffers from significant inter-observer variability. This problem could be solved by the fully automated deep learning system developed by Wouter Bulten and his colleagues. Their work appeared online today in The Lancet Oncology. Read more →
Computational Pathology Group
The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.
Vacancies & Student projects
Are you a creative researcher with a PhD degree in medical image analysis, computer vision, machine learning or similar? Do you want to contribute to the implementation of a algorithm-supported workflow for digital gastrointestinal pathology, which will increase the time of pathologists for complex diagnostics and reduce the wait time for patients? Then we are looking for you!
Are you looking for a job next to your studies and do you have affinity with artificial intelligence and medical images? Then don't miss this opportunity! The Computational Pathology group is looking for a student assistant to assist with ground truth generation for training of artificial neural networks and preparation of slides for digitization.
Aim of the presently proposed ‘proof of concept‘ study is to develop digital pattern recognition algorithms (more specifically: deep neural networks) for the extraction of morphological features from scanned H&E stained tissue sections from TNBC which are indicative for the risk of recurrence.