Maschenka Balkenhol et al assessed the prognostic value of absolute mitotic counts for triple negative breast cancer, using both deep learning and manual procedures. Yesterday their work appeared online in Cellular Oncology. 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.
Vacancies & Student projects
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.
The Computational Pathology Group of the Radboud University Medical Center, Nijmegen, The Netherlands, is seeking a Postdoctoral researcher with experience in development of deep learning models. This is an excellent opportunity to develop cutting-edge deep learning technology to have an impact on breast cancer research and personalized cancer treatment.