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 →
Computational Pathology Group
The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.
CPG launches new spin-off, Aiosyn, to accelerate the implementation of AI in Pathology Read more →
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 →
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 →
Vacancies & Student projects
PhD candidate ‘Development of explainable AI methods for end-to-end-learning with gigapixel images in the BigPicture consortium’
PhD position for developing novel, explainable machine learning algorithms for end-to-end learning with gigapixel pathology images in collaboration with the BigPicture consortium
PhD candidate ‘Improving care for prostate cancer patient through AI-driven radiology/pathology fusion’
PhD position for developing algorithms to fuse pathology/radiology for improve prostate cancer diagnostics.
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.
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 →