Joep Bogaerts' presentation on the international Delphi study won the second prize at the European Congress of Pathology. 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
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.
Predict neoadjuvant chemotherapy response in breast cancer histopathology from a panel of immunohistochemical markers
Development of AI-biomarkers to predict neoaduvant chemotherapy response in breast cancer.
Development of a deep learning system to predict BC risk in H&E
We aim to combine advanced CT and FDG-PET image analysis with computational pathology to predict the most optimal treatment for each individual patient.Read more →