Results of the PANDA challenge published in Nature Medicine
After two years of hard work, the final results of PANDA Challenge on AI for prostate cancer grading are published! Read more →
Computational Pathology Group
The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.
News
ERC Starting Grant for Geert Litjens
The European Research Council awarded Geert Litjens with a grant for his project "AIS-CaP: Interpretable Artificial Intelligence across Scales for Next-Generation Cancer Prognostics". Read more →
DKF Kolff+ Success Accelerator Grant for improved kidney transplant diagnostics using AI
The DKF awarded the grant to CPG’s Meyke Hermsen, Dominique van Midden, and Jeroen van der Laak for their proposal to use AI for improved histopathologic kidney biopsy assessment. Read more →
Francesco Ciompi and Chella van der Post receive Hanarth Foundation grant to improve detection of diffuse gastric cancer
Francesco Ciompi and Chella van der Post have been awarded 400,000 euros for the execution of their project aimed to develop AI solutions for improving DGC diagnostics. Read more →
New start-up Aiosyn will bring AI research into pathology diagnostics
CPG launches new spin-off, Aiosyn, to accelerate the implementation of AI in Pathology Read more →
Geert Litjens receives VIDI grant to build an AI bridge between pathology and radiology
Geert Litjens has been awarded a Vidi grant worth 800,000 euros by the Dutch Research Council. He will investigate AI solutions for prostate cancer by combining radiology and pathology images. Read more →
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 →
Review on Computational Pathology published in Nature Medicine
Jeroen van der Laak, Geert Litjens, and Francesco Ciompi discuss the way to the clinic for computation pathology algorithms. Read more →
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.
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.
Student project
Unmix multiplex immunohistochemistry with deep learning
Development of a deep learning system to unmix IHC stainings with multiple colors
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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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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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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