Predict neoadjuvant chemotherapy response in breast cancer histopathology from a panel of immunohistochemical markers

Predict neoadjuvant chemotherapy response in breast cancer histopathology from a panel of immunohistochemical markers
This application has been filled. Please see our page for open vacancies.
Invasive breast cancer is increasingly treated with neoadjuvant (i.e., pre-operative) chemotherapy. However, it is effective only for some patients.
Develop biomarkers based on the joint analysis of multiple digital pathology whole-slide images of pre-operative breast cancer biopsies stained with hematoxylin and eosin (H&E) and a panel of immunohistochemical (IHC) markers to predict treatment response.
Develop deep learning systems to quantify biomarkers in IHC slides
Apply existing deep learning models for the extraction of some biomarkers
Align extracted features via registration across multiple slides
Use extracted features to build a prediction model of treatment response

Students with a major in computer science, biomedical engineering, artificial intelligence, physics, or a related area in the final stage of master level studies are invited to apply.
Affinity with programming in Python
Interest in deep learning and medical image analysis

Location: Radboud University Nijmegen Medical Center
For more information please contact