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 →
Computational Pathology Group
The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.
News
Fifty thousand euros raised for the fight against cancer, including the research of Daan Geijs
On the 1st of July 2022, nearly 100 people have committed themselves to cycling together to raise money for important cancer research, including the research of Daan Geijs. Read more →
Francesco Ciompi appointed Associate Professor of Computational Pathology
Starting the 1st of June 2022, Francesco Ciompi is appointed Associate Professor of Computational Pathology at the Radboud UMC. 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
AQUILA
The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.
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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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DCIS
The aim of this project is to use deep learning for histological assessment of the stroma for improved risk stratification of ductal carcinoma in situ (DCIS) patients.
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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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PROACTING
The aim of PROACTING is to predict neoadjuvant chemotherapy treatment response from a single pre-operative core-needle biopsy of breast cancer tissue.
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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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SysMIFTA
The investigation of the role of immune cell subsets in interstitial fibrosis and tubular atrophy in renal allografts, using multiplex immunohistochemistry and Deep Learning.
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Automated Quantification of Tumor-Infiltrating Lymphocytes
Developing an algorithmn that can automatically detect and segment tumor-infiltrating lymphocytes in breast 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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