Computational Pathology Group

The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.

Automated tumor detection
Automated tumor detection

News

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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.

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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.

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Student project

Unmix multiplex immunohistochemistry with deep learning

Development of a deep learning system to unmix IHC stainings with multiple colors

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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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DIAGGRAFT

Investigation of AI for kidney transplant pathology.

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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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PANCAIM

Investigation of AI for pancreatic cancer in radiology, pathology, genomics.

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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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Members

Clément Grisi

Clément Grisi

PhD Candidate

Cristian Tommasino

Cristian Tommasino

PhD Candidate

Daan Geijs

Daan Geijs

PhD Candidate

Daan Schouten

Daan Schouten

PhD Candidate

Daan Zegers

Daan Zegers

Student assistant

Eva Cuppen

Eva Cuppen

Student assistant

Francesco Ciompi

Francesco Ciompi

Associate Professor

Gabriel Silva de Souza

Gabriel Silva de Souza

Research Assistant

Geert Litjens

Geert Litjens

Assistant Professor

Hans Pinckaers

Hans Pinckaers

PhD Candidate

Hiba Qoubbane

Hiba Qoubbane

Student assistant

Jasper Linmans

Jasper Linmans

PhD Candidate

Jianpeng An

Jianpeng An

PhD Candidate

Joep Bogaerts

Joep Bogaerts

PhD Candidate

Joey Spronck

Joey Spronck

PhD Candidate

John-Melle Bokhorst

John-Melle Bokhorst

PhD Candidate

Khrystyna Faryna

Khrystyna Faryna

PhD Candidate

Leander van Eekelen

Leander van Eekelen

PhD Candidate

Leslie Tessier

Leslie Tessier

PhD Candidate

Marina D'Amato

Marina D'Amato

PhD Candidate

Mark E. Sherman

Mark E. Sherman

Visiting Professor

Mart van Rijthoven

Mart van Rijthoven

PhD Candidate

Maschenka Balkenhol

Maschenka Balkenhol

Pathology Resident and Postdoctoral Researcher

Matthijs Luijten

Matthijs Luijten

Master Student

Merijn van Erp

Merijn van Erp

Scientific Programmer

Meyke Hermsen

Meyke Hermsen

PhD Candidate / Study manager

Micael Karlberg

Micael Karlberg

Postdoctoral Researcher

Michelle Stegeman

Michelle Stegeman

Master Student

Miriam Groeneveld

Miriam Groeneveld

Research Software Engineer

Muradije Demirel

Muradije Demirel

Research Technician

Myrthe van de Ven

Myrthe van de Ven

Student assistant

Péter Bándi

Péter Bándi

PhD Candidate

Roan Sherif

Roan Sherif

Student assistant

Robin Lomans

Robin Lomans

PhD Candidate

Sanne Vermorgen

Sanne Vermorgen

Pathology Resident

Stephan Dooper

Stephan Dooper

PhD Candidate

Tariq Haddad

Tariq Haddad

PhD Candidate

Tessa de Wijs

Tessa de Wijs

Student assistant

Thijs Gelton

Thijs Gelton

Master Student

Thomas de Bel

Thomas de Bel

PhD Candidate

Tinka Santing

Tinka Santing

Student assistant

Valerie Dechering

Valerie Dechering

Research Technician

Witali Aswolinskiy

Witali Aswolinskiy

Postdoctoral Researcher