Daan Geijs' short-talk on the development of a Mohs surgery AI tool was reviewed as second-best during the annual PhD retreat of RIHS. Read more →
Computational Pathology Group
The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.
From February 1, 2022, Jeroen van der Laak was appointed full professor of computational Pathology at the Radboud UMC. Read more →
Wouter Bulten succesfully defended his PhD thesis titled 'Artificial intelligence as a digital fellow in pathology: human-machine synergy for improved prostate cancer diagnosis.' on the 28th of January. Read more →
After two years of hard work, the final results of PANDA Challenge on AI for prostate cancer grading are published! Read more →
The European Research Council awarded Geert Litjens with a grant for his project "AIS-CaP: Interpretable Artificial Intelligence across Scales for Next-Generation Cancer Prognostics". Read more →
The DKF awarded the grant to CPG’s Meyke Hermsen, Dominique van Midden, and Jeroen van der Laak for their proposal to use AI for improved histopathologic kidney biopsy assessment. Read more →
Francesco Ciompi and Chella van der Post have been awarded 400,000 euros for the execution of their project aimed to develop AI solutions for improving DGC diagnostics. Read more →
CPG launches new spin-off, Aiosyn, to accelerate the implementation of AI in Pathology Read more →
Vacancies & Student projects
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.
Predict neoadjuvant chemotherapy response in breast cancer histopathology from a panel of immunohistochemical markers
Development of AI-biomarkers to predict neoaduvant chemotherapy response in breast cancer.
Development of a deep learning system to unmix IHC stainings with multiple colors
We aim to combine advanced CT and FDG-PET image analysis with computational pathology to predict the most optimal treatment for each individual patient.Read more →
Developing an algorithmn that can automatically detect and segment tumor-infiltrating lymphocytes in breast cancer.Read more →