The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.
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 →
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 →
Jeroen van der Laak, Geert Litjens, and Francesco Ciompi discuss the way to the clinic for computation pathology algorithms. Read more →
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 →
The Innovative Medicine Initiative has awarded a 70 MEuro project to build the largest integrated database of digitized histopathologic slides and AI algorithms in the world. Read more →
The Fiets Pieten cycled a total of 355 km across the country, solving a special CPG mystery with help of their fellow crew members along the way. Read more →
Francesco Ciompi of the Computational Pathology group has received a prestigious NWO-TTW VIDI grant of 800,000 euro for his project "Predicting Lung Cancer Immunotherapy Response. It's personal". Read more →
In this project we aim to develop and validate AI techniques for the detection of serous tubal intra-epithelial carcinoma (STIC), a non-invasive lesion in the distal Fallopian tube which is expected to be a precursor for ovarian cancer. Read more →
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.
Development of AI-biomarkers to predict neoaduvant chemotherapy response in breast cancer.
Development of a deep learning system to unmix IHC stainings with multiple colors
The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.
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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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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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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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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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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 aims to build an open data ecosystem for cell migration research, through standardization, dissemination and meta-analysis efforts.
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Challenge on prostate cancer grading of biopsies using the Gleason grading system.
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Detecting biomarkers for improved prognosis for triple negative breast cancer by combining histopathology, multiplex immunohistochemistry and Deep Learning.
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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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Project with focus on the extension of open-source software ASAP that includes functionality for study and case management.
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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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Developing an algorithmn that can automatically detect and segment tumor-infiltrating lymphocytes in breast cancer.
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We aim at developing automatic and reproducible quantification of Tumor-Stroma Ratio in Whole-Slide Images using Deep Learning.
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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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