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.
Vacancies & Student projects
There are no vacancies at the moment.
We welcome good master's and bachelor's students to perform academic research in our group. We offer various projects that can be tuned to match your thesis requirements. You can also browse through our research pages to read about the different research topics of our group.
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.
The positions below are closed, please do not apply. They are listed to give you an idea of the kind of positions we regularly offer.Vacancies