A large part of DIAG focuses on the analysis of pathology images and is embedded in the Department of Pathology of Radboudumc and has a strong translational focus. The history of the group reaches back to the early 1980s, and in 2013 the group joined DIAG and started to grow rapidly, facilitated by the simultaneous advents of digital pathology and deep learning.

Projects focus on two areas:

  1. increasing efficiency of the current pathology diagnostic process by developing and validating computer aided diagnosis (CAD) workflows;
  2. supporting personalized medicine by developing image based biomarkers that are predictive for patient outcome or therapy response. The latter may be based either on H&E stained tissue sections or immunohistochemistry.

Below you will find an overview of current research projects. More details can be found on the Computational Pathology Group's own site.


Deep PCA

In this project, we will combine deep learning and digitized whole-slide imaging of prostate cancer for reproducible extraction of quantitative biomarkers.

Read more →

The aim of this project is to use deep learning for histological assessment of the stroma for improved risk stratification of ductal carcinoma in situ (DCIS) patients.

Read more →
Deep Derma

The aim of this project is to apply artificial intelligence to detect basal cell carcinoma.

Read more →

The investigation of the role of immune cell subsets in interstitial fibrosis and tubular atrophy in renal allografts, using multiplex immunohistochemistry and Deep Learning.

Read more →
Tumor budding

In this project, we will develop and validate digital image analysis algorithms for quantification of tumor budding from scanned whole slide images.

Read more →

The aim of PROACTING is to predict neoadjuvant chemotherapy treatment response from a single pre-operative core-needle biopsy of breast cancer tissue.

Read more →