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

Vacancies & Student projects

Vacancy

Crossing the boundaries: machine learning for pancreatic cancer across radiology, pathology and genetics (PANCAIM)

We are offering three PhD positions to apply machine learning to varying diagnostic modality to improve treatment for pancreatic cancer patients

More information

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.

More information

Projects

AQUILA

The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.

Read more →
Artifact detection in digitized histopathology images

Development of a deep learning algorithm that can classify the different types of artifacts in whole slide images.

Read more →
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.

Read more →
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.

Read more →
Deep PCA

In this project, we will combine deep learning and digitized whole-slide imaging of prostate cancer for reproducible extraction of quantitative biomarkers.

Read more →
ExaMode

The aim of ExaMode is to collect training data with limited human interaction for the processing of exascale volumes of healthcare data.

Read more →
Multimot

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

Read more →
PANDA Challenge

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

Read more →
PRIM4BC

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

Read more →
PROACTING

The aim of PROACTING is to predict neoadjuvant chemotherapy treatment response from a single pre-operative core-needle biopsy of breast cancer tissue.

Read more →
STITPRO II

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

Read more →
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.

Read more →
Tumor-Stroma Ratio

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

Read more →
Tumor budding

In this project, we will develop and validate digital image analysis algorithms for quantification of tumor budding from scanned whole slide images.

Read more →

Members

Caner Mercan

Caner Mercan

Postdoctoral Researcher

Daan Geijs

Daan Geijs

PhD Candidate

David Tellez

David Tellez

PhD Candidate

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

Gijs Smit

Gijs Smit

Master Student

Hans Pinckaers

Hans Pinckaers

PhD Candidate

Jasper Linmans

Jasper Linmans

PhD Candidate

Jeffrey Hoven

Jeffrey Hoven

Bachelor Student

Jeroen van der Laak

Jeroen van der Laak

Associate Professor

Jeroen Vermazeren

Jeroen Vermazeren

Master Student

John-Melle Bokhorst

John-Melle Bokhorst

PhD Candidate

Luca Meesters

Luca Meesters

Student assistant

Ludo van Alst

Ludo van Alst

Master Student

Mariam Baghdady

Mariam Baghdady

Student assistant

Mart van Rijthoven

Mart van Rijthoven

PhD Candidate

Maschenka Balkenhol

Maschenka Balkenhol

Pathology Resident and Researcher

Merijn van Erp

Merijn van Erp

Scientific Programmer

Meyke Hermsen

Meyke Hermsen

PhD Candidate

Milly van de Warenburg

Milly van de Warenburg

Student assistant

Miriam Groeneveld

Miriam Groeneveld

Research Software Engineer

Niels van den Hork

Niels van den Hork

Master Student

Nikki Wissink

Nikki Wissink

Student assistant

Péter Bándi

Péter Bándi

PhD Candidate

Sophie van den Broek

Sophie van den Broek

Student assistant

Tariq Haddad

Tariq Haddad

PhD Candidate

Thomas de Bel

Thomas de Bel

PhD Candidate

Valerie Dechering

Valerie Dechering

Bachelor Student

Witali Aswolinskiy

Witali Aswolinskiy

Postdoctoral Researcher

Wouter Bulten

Wouter Bulten

PhD Candidate