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

PhD defense David Tellez

PhD defense David Tellez

David Tellez succesfully defended his PhD thesis with the title 'Advancing computational pathology with deep learning: from patches to gigapixel image-level classification'. Read more →

Academic Alliance grant for development of Mohs surgery AI tool

Academic Alliance grant for development of Mohs surgery AI tool

The Academic Alliance Fund of Radboudumc and Maastricht UMC+ awarded Geert Litjens, Daan Geijs, Avital Amir, Lisa Hillen, Veronique Winnepenninckx and Nicole Kelleners a grant of 100,000 euros for their project proposal entitled: ‘Mohs chirurgy supported by artificial intelligence; better, faster, cheaper’. Read more →

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Vacancies & Student projects

Vacancy

Postdoc 'Deep learning to improve pathology diagnostics'

Are you a creative researcher with a PhD degree in medical image analysis, computer vision, machine learning or similar? Are you looking for an opportunity to develop cutting-edge deep learning technology to have an impact on cancer research and personalized cancer treatment? Then we are looking for you!

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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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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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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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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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Multimot

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

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PANDA Challenge

Challenge on prostate cancer grading of biopsies using the Gleason grading system.

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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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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-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

Cyril de Kock

Cyril de Kock

Master Student

Daan Geijs

Daan Geijs

PhD Candidate

Dominique van Midden

Dominique van Midden

Pathology Resident

Eva Cuppen

Eva Cuppen

Student assistant

Fazael Ayatollahi

Fazael Ayatollahi

Postdoctoral Researcher

Francesco Ciompi

Francesco Ciompi

Assistant Professor

Gabriel Silva de Souza

Gabriel Silva de Souza

Research Assistant

Geert Litjens

Geert Litjens

Assistant Professor

Hans Pinckaers

Hans Pinckaers

PhD Candidate

Jasper Linmans

Jasper Linmans

PhD Candidate

Jeroen van der Laak

Jeroen van der Laak

Associate Professor

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

Kim Wolffenbuttel

Kim Wolffenbuttel

Student assistant

Leander van Eekelen

Leander van Eekelen

PhD Candidate

Leslie Tessier

Leslie Tessier

PhD Candidate

Mariam Baghdady

Mariam Baghdady

Student assistant

Mart van Rijthoven

Mart van Rijthoven

PhD Candidate

Maschenka Balkenhol

Maschenka Balkenhol

Pathology Resident and Postdoctoral Researcher

Maxime Sülter

Maxime Sülter

Bachelor Student

Menno Hackmann

Menno Hackmann

Bachelor Student

Merijn van Erp

Merijn van Erp

Scientific Programmer

Meyke Hermsen

Meyke Hermsen

PhD Candidate

Micael Karlberg

Micael Karlberg

Postdoctoral Researcher

Miriam Groeneveld

Miriam Groeneveld

Research Software Engineer

Muradije Demirel

Muradije Demirel

Research Technician

Myrthe van de Ven

Myrthe van de Ven

Student assistant

Nikki Wissink

Nikki Wissink

Student assistant

Péter Bándi

Péter Bándi

PhD Candidate

Stephan Dooper

Stephan Dooper

PhD Candidate

Tariq Haddad

Tariq Haddad

PhD Candidate

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

Wouter Bulten

Wouter Bulten

PhD Candidate