
AI for Health Symposium
On November 2, Radboudumc AI for health organized a symposium on the impact of AI on healthcare. Read more →
The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.
On November 2, Radboudumc AI for health organized a symposium on the impact of AI on healthcare. Read more →
Joep Bogaerts' presentation on the international Delphi study won the second prize at the European Congress of Pathology. Read more →
The journal paper describing the Medical Segmentation Decathlon challenge has been published in Nature Communications. Read more →
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.
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
The goal of Bigpicture is to accelerate the development of AI in pathology by providing a large repository of high-quality annotated pathology data, accessible in a responsible, inclusive and sustainable way.
Read more →The aim of this project is to apply artificial intelligence to detect basal cell carcinoma.
Read more →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 ExaMode is to collect training data with limited human interaction for the processing of exascale volumes of healthcare data.
Read more →The goal of IGNITE is to use automatic biomarker extraction with deep learning to predict the response of non-small cell lung cancer patients to immunotherapy.
Read more →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 →The aim of this project is to integrate histopathological and radiological images to improve our understanding of disease diagnosis and progression in prostate cancer.
Read more →The aim of STIC is to improve the diagnostics of precursor lesions to high grade serous carcinoma (HGSC), the most common and lethal form of ovarian cancer.
Read more →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 UNESCO is to study techniques for estimating uncertainty in computational pathology.
Read more →The aim of UNIC is to develop artificial intelligence methods to refine diffuse-type gastric cancer (DGC) diagnostics.
Read more →