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

Note: This application has been filled. Please see our Vacancies page for open vacancies.

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

This is an AI for Health MSc project. Students are elgible to receive a monthly reimbursement of €500,- for a period of six months. For more information please read the requirements.

Clinical Problem

Invasive breast cancer is increasingly treated with neoadjuvant (i.e., pre-operative) chemotherapy. However, it is effective only for some patients.

Solution

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.

Data

Embedding

Requirements

  • 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

Information

  • Project duration: 6 months
  • Location: Radboud University Nijmegen Medical Center
  • For more information please contact Witali Aswolinskiy or Francesco Ciompi

People

Witali Aswolinskiy

Witali Aswolinskiy

Postdoctoral Researcher

Computational Pathology Group

Francesco Ciompi

Francesco Ciompi

Associate Professor

Computational Pathology Group