Congratulations to Thomas de Bel for winning the Best Poster Award at the second edition of the International Conference on Medical Imaging with Deep Learning held in London this week. Read more →
Computational Pathology Group
The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology and focusing on computer-aided diagnosis. Application areas include diagnostics and prognostics of breast, prostate and colon cancer. We have rapidly expanded over the last few years, counting over 15 people today. Our group is among the international front runners in the field, witnessed for instance by our highly successful CAMELYON challenges. We have a strong translational focus, facilitated by our close collaboration with clinicians and industry.
The final meeting of the AMI-project took place last week. The AMI-project was a close collaborative project between the Diagnostic Image Analysis Group and the Fraunhofer Institute for Digital Medicine MEVIS. With the development of a generic platform for automatic medical image analysis, the project was a succes. Read more →
Vacancies & Student projects
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.
The goal of this project is to develop a deep learning model that can detect and classify the different types of artifacts observed in whole slide images.
The goal is to develop a deep learning algorithm for the differentiation of STIC lesions from normal fallopian tube epithelium. The digital pathology images of a cohort of both STIC lesions and normal tubal epithelium from BRCA1/2 carries will be available. The output will be an algorithm that can identify aberrant tubal epithelium with a high sensitivity.
Aim of the proposed project is to develop deep learning algorithms for the extraction of features from scanned H&E and immunohistochemically stained tissue sections from lung cancer which carry predictive value in the context of immune therapy treatment response.