Computational Pathology Group

The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology and focusing on computer-aided diagnosis. Application areas include diagnostics and prognostics of breast, prostate and colon cancer. We have rapidly expanded over the last few years, counting over 15 people today. Our group is among the international front runners in the field, witnessed for instance by our highly successful CAMELYON challenges. We have a strong translational focus, facilitated by our close collaboration with clinicians and industry.

Automated tumor detection
Automated tumor detection

Highlights

Nathan Kaufman timely topics lecture for Jeroen van der Laak at USCAP 2019

Nathan Kaufman timely topics lecture for Jeroen van der Laak at USCAP 2019

Jeroen van der Laak was honored as Nathan Kaufman timely topics lecturer at the 108th annual meeting of the United States and Canadian Academy of Pathology (USCAP). His keynote lecture titled 'The Rise of Artificial Intelligence: Impact on Advanced Diagnostics in Health Care' was held inside the impressive Potomac Ballroom … Read more →

JBHI's special issue 'Computational Pathology' now accepting submissions

JBHI's special issue 'Computational Pathology' now accepting submissions

The goal of this special issue is to attract and highlight the latest developments in computational pathology, and feature papers proposing state-of-the-art solutions in the field of digital pathology using advanced image analysis and artificial intelligence. Topics of interest include, but are not limited to: Artificial intelligence and Deep Learning … Read more →

Vacancies & Student projects

Student project

Deep Learning for the differentiation of STIC lesions

The goal is to develop a deep learning algorithm for the differentiation of STIC lesions from normal fallopian tube epithelium. The digital pathology images of a cohort of both STIC lesions and normal tubal epithelium from BRCA1/2 carries will be available. The output will be an algorithm that can identify aberrant tubal epithelium with a high sensitivity.

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Vacancy

Postdoctoral Researcher for Deep Learning in Computational Pathology

The Computational Pathology Group of the Radboud University Medical Center, Nijmegen, The Netherlands, is seeking a Postdoctoral researcher with experience in development of deep learning models. This is an excellent opportunity to develop cutting-edge deep learning technology to have an impact on breast cancer research and personalized cancer treatment.

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Vacancy

Research Software Engineer in Digital Pathology

The Diagnostic Image Analysis Group (DIAG) of the Radboud University Medical Center, Nijmegen, The Netherlands, is seeking a research software engineer to join our growing team. This is an excellent opportunity to have an impact on the field by translating our state-of-the-art deep learning research in pathology into robust software that could be integrated into the pathologists workflow.

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Projects

AMI

Joint project between DIAG and Fraunhofer MEVIS aimed at the development of a generic platform for automatic medical image analysis.

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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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CAMELYON17

ISBI 2017 challenge to evaluate algorithms for automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections.

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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 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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Multimot

MULTIMOT aims to build an open data ecosystem for cell migration research, through standardization, dissemination and meta-analysis efforts.

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PRIM4BC

Detecting biomarkers for improved prognosis for triple negative breast cancer by combining histopathology, multiplex immunohistochemistry and Deep Learning.

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STITPRO II

Project with focus on the extension of open-source software ASAP that includes functionality for study and case management.

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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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Tumor-Stroma Ratio

We aim at developing automatic and reproducible quantification of Tumor-Stroma Ratio in Whole-Slide Images using Deep Learning.

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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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Members

David Tellez

David Tellez

PhD student

Elke Loskamp-Huntink

Elke Loskamp-Huntink

Study manager

Emiel Stoelinga

Emiel Stoelinga

Master student

Francesco Ciompi

Francesco Ciompi

Assistant Professor

Geert Litjens

Geert Litjens

Assistant professor

Germonda Mooij

Germonda Mooij

Master student

Hans Pinckaers

Hans Pinckaers

PhD student

Jasper Linmans

Jasper Linmans

PhD student

Jeroen van der Laak

Jeroen van der Laak

Associate professor/Group leader

John-Melle Bokhorst

John-Melle Bokhorst

PhD student

Karel Gerbrands

Karel Gerbrands

Research Software Engineer

Koen Dercksen

Koen Dercksen

Master student

Mart van Rijthoven

Mart van Rijthoven

PhD student

Maschenka Balkenhol

Maschenka Balkenhol

Pathology resident and PhD student

Maud Wekking

Maud Wekking

Research technician

Merijn van Erp

Merijn van Erp

Scientific programmer

Meyke Hermsen

Meyke Hermsen

PhD student

Michel Kok

Michel Kok

Master student

Oscar Geessink

Oscar Geessink

PhD student

Patrick Sonsma

Patrick Sonsma

Master student

Péter Bándi

Péter Bándi

PhD student

Thomas de Bel

Thomas de Bel

PhD student

Wouter Bulten

Wouter Bulten

PhD student

Yiping Jiao

Yiping Jiao

PhD student

Zaneta Swiderska-Chadaj

Zaneta Swiderska-Chadaj

Postdoctoral researcher