In this project we aim to develop and validate AI techniques for the detection of serous tubal intra-epithelial carcinoma (STIC), a non-invasive lesion in the distal Fallopian tube which is expected to be a precursor for ovarian cancer. 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
Closing the gap: Implementation of AI-based breast and prostate cancer grading algorithms in clinical practice
We are offering a PhD position for implementation of AI-based prostate and breast cancer grading in clinical practice
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.
Development of a deep learning algorithm that can classify the different types of artifacts in whole slide images.Read more →