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

Highlights

KNAW Early Career Award for Geert Litjens

KNAW Early Career Award for Geert Litjens

The KNAW Early Career Award is intended to showcase talented PhD graduates, and to provide them with support and encouragement. The Award is aimed at researchers at the start of their career who are capable of developing innovative and original research ideas. Read more →

All highlights

Vacancies & Student projects

Vacancy

Postdoc 'Deep learning in digital gastrointestinal pathology'

Are you a creative researcher with a PhD degree in medical image analysis, computer vision, machine learning or similar? Do you want to contribute to the implementation of a algorithm-supported workflow for digital gastrointestinal pathology, which will increase the time of pathologists for complex diagnostics and reduce the wait time for patients? Then we are looking for you!

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Vacancy

Student assistant Computational Pathology

Are you looking for a job next to your studies and do you have affinity with artificial intelligence and medical images? Then don't miss this opportunity! The Computational Pathology group is looking for a student assistant to assist with ground truth generation for training of artificial neural networks and preparation of slides for digitization.

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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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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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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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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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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

Caner Mercan

Caner Mercan

Postdoctoral researcher

David Tellez

David Tellez

PhD student

Francesco Ciompi

Francesco Ciompi

Assistant Professor

Geert Litjens

Geert Litjens

Assistant professor

Hans Pinckaers

Hans Pinckaers

PhD student

Jasper Linmans

Jasper Linmans

PhD student

Jeffrey Hoven

Jeffrey Hoven

Bachelor student

Jeroen van der Laak

Jeroen van der Laak

Associate professor

Leander van Eekelen

Leander van Eekelen

Master student

Ludo van Alst

Ludo van Alst

Master 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

Miriam Groeneveld

Miriam Groeneveld

Research Software Engineer

Niels van den Hork

Niels van den Hork

Master student

Oscar Geessink

Oscar Geessink

PhD student

Péter Bándi

Péter Bándi

PhD student

Sven-Patrik Hallsjö

Sven-Patrik Hallsjö

Postdoctoral researcher

Tariq Haddad

Tariq Haddad

PhD student

Thomas de Bel

Thomas de Bel

PhD student

Tristan Payer

Tristan Payer

Master student

Witali Aswolinskiy

Witali Aswolinskiy

Postdoctoral researcher

Wouter Bulten

Wouter Bulten

PhD student

Zaneta Swiderska-Chadaj

Zaneta Swiderska-Chadaj

Postdoctoral researcher