AI for Health Symposium
On November 2, Radboudumc AI for health organized a symposium on the impact of AI on healthcare. Read more →
Computational Pathology Group
The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.
News
Delphi study on STIC diagnosis wins 2nd Best Oral Prize at the 34th European Congress of Pathology
Joep Bogaerts' presentation on the international Delphi study won the second prize at the European Congress of Pathology. Read more →
Results of the Medical Segmentation Decathlon challenge published in Nature Communications
The journal paper describing the Medical Segmentation Decathlon challenge has been published in Nature Communications. Read more →
Vacancies & Student projects
Student project
Deep Learning to predict recurrence in triple negative breast cancer (TNBC)
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.
Student project
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.
Student project
Unmix multiplex immunohistochemistry with deep learning
Development of a deep learning system to unmix IHC stainings with multiple colors
Projects
BIGPICTURE
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.
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Deep Derma
The aim of this project is to apply artificial intelligence to detect basal cell carcinoma.
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Deep PCA
In this project, we will combine deep learning and digitized whole-slide imaging of prostate cancer for reproducible extraction of quantitative biomarkers.
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ExaMode
The aim of ExaMode is to collect training data with limited human interaction for the processing of exascale volumes of healthcare data.
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IGNITE
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.
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GENERATOR: Optimal treatment prediction for early stage lung cancer
We aim to combine advanced CT and FDG-PET image analysis with computational pathology to predict the most optimal treatment for each individual patient.
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PathRad Fusion
The aim of this project is to integrate histopathological and radiological images to improve our understanding of disease diagnosis and progression in prostate cancer.
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STIC
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.
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Tumor budding
In this project, we will develop and validate digital image analysis algorithms for quantification of tumor budding from scanned whole slide images.
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UNESCO
The aim of UNESCO is to study techniques for estimating uncertainty in computational pathology.
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UNIC
The aim of UNIC is to develop artificial intelligence methods to refine diffuse-type gastric cancer (DGC) diagnostics.
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