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.
Development of deep learning models for segmentation of macrophages in H&E-stained slides of non-small cell lung cancer.
Development of a deep learning algorithm for automated diagnosis of DRESS syndrome
Use quantified pathomics features to help improve survival prediction for PDAC patients
Development of deep learning methods to characterize PD-L1 positive cells in lung cancer immunohistochemistry for automatic biomarker extraction
Development of a deep learning algorithm for automated whole-slide pathology image analysis and quality control
Development of deep learning methods to combine Tumor-Infiltrating Lymphocytes (TILs) and Tertiary Lymphoid Structures (TLS) in Non-Small Cell Lung Cancer histopathology images
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