The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.
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 →
CPG launches new spin-off, Aiosyn, to accelerate the implementation of AI in Pathology Read more →
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 →
PhD position for developing novel, explainable machine learning algorithms for end-to-end learning with gigapixel pathology images in collaboration with the BigPicture consortium
PhD position for developing algorithms to fuse pathology/radiology for improve prostate cancer diagnostics.
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.
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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