Geert Litjens has been awarded a Vidi grant worth 800,000 euros by the Dutch Research Council. He will investigate AI solutions for prostate cancer by combining radiology and pathology images. Read more →
Computational Pathology Group
The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.
David Tellez succesfully defended his PhD thesis with the title 'Advancing computational pathology with deep learning: from patches to gigapixel image-level classification'. Read more →
Jeroen van der Laak, Geert Litjens, and Francesco Ciompi discuss the way to the clinic for computation pathology algorithms. Read more →
The Academic Alliance Fund of Radboudumc and Maastricht UMC+ awarded Geert Litjens, Daan Geijs, Avital Amir, Lisa Hillen, Veronique Winnepenninckx and Nicole Kelleners a grant of 100,000 euros for their project proposal entitled: ‘Mohs chirurgy supported by artificial intelligence; better, faster, cheaper’. Read more →
The Innovative Medicine Initiative has awarded a 70 MEuro project to build the largest integrated database of digitized histopathologic slides and AI algorithms in the world. Read more →
The Fiets Pieten cycled a total of 355 km across the country, solving a special CPG mystery with help of their fellow crew members along the way. Read more →
Francesco Ciompi of the Computational Pathology group has received a prestigious NWO-TTW VIDI grant of 800,000 euro for his project "Predicting Lung Cancer Immunotherapy Response. It's personal". Read more →
In this project we aim to develop and validate AI techniques for the detection of serous tubal intra-epithelial carcinoma (STIC), a non-invasive lesion in the distal Fallopian tube which is expected to be a precursor for ovarian cancer. 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 →