The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology and focusing on computer-aided diagnosis. Application areas include diagnostics and prognostics of breast, prostate and colon cancer. We have rapidly expanded over the last few years, counting over 15 people today. Our group is among the international front runners in the field, witnessed for instance by our highly successful CAMELYON challenges. We have a strong translational focus, facilitated by our close collaboration with clinicians and industry.
Jeroen van der Laak was honored as Nathan Kaufman timely topics lecturer at the 108th annual meeting of the United States and Canadian Academy of Pathology (USCAP). His keynote lecture titled 'The Rise of Artificial Intelligence: Impact on Advanced Diagnostics in Health Care' was held inside the impressive Potomac Ballroom … Read more →
The goal of this special issue is to attract and highlight the latest developments in computational pathology, and feature papers proposing state-of-the-art solutions in the field of digital pathology using advanced image analysis and artificial intelligence. Topics of interest include, but are not limited to: Artificial intelligence and Deep Learning … Read more →
The goal is to develop a deep learning algorithm for the differentiation of STIC lesions from normal fallopian tube epithelium. The digital pathology images of a cohort of both STIC lesions and normal tubal epithelium from BRCA1/2 carries will be available. The output will be an algorithm that can identify aberrant tubal epithelium with a high sensitivity.
The Computational Pathology Group of the Radboud University Medical Center, Nijmegen, The Netherlands, is seeking a Postdoctoral researcher with experience in development of deep learning models. This is an excellent opportunity to develop cutting-edge deep learning technology to have an impact on breast cancer research and personalized cancer treatment.
The Diagnostic Image Analysis Group (DIAG) of the Radboud University Medical Center, Nijmegen, The Netherlands, is seeking a research software engineer to join our growing team. This is an excellent opportunity to have an impact on the field by translating our state-of-the-art deep learning research in pathology into robust software that could be integrated into the pathologists workflow.
Joint project between DIAG and Fraunhofer MEVIS aimed at the development of a generic platform for automatic medical image analysis.
Read more →The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.
Read more →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 →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 →In this project, we will combine deep learning and digitized whole-slide imaging of prostate cancer for reproducible extraction of quantitative biomarkers.
Read more →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 aims to build an open data ecosystem for cell migration research, through standardization, dissemination and meta-analysis efforts.
Read more →Detecting biomarkers for improved prognosis for triple negative breast cancer by combining histopathology, multiplex immunohistochemistry and Deep Learning.
Read more →Project with focus on the extension of open-source software ASAP that includes functionality for study and case management.
Read more →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 →We aim at developing automatic and reproducible quantification of Tumor-Stroma Ratio in Whole-Slide Images using Deep Learning.
Read more →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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