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Der “Janssen Special Award” wird für Projekte mit besonderer Relevanz für die Gesundheitsversorgung in Zukunft vergeben.
unterstützt durch:
Deep learning for histological image classification
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Kontakt:
Rupert Ecker, PhD rupert.ecker@tissuegnostics.com
Amirreza Mahbod amirreza.mahbod@tissuegnostics.com
Robert Nica robert.nica@tissuegnostics.com
Catalin Captarencu catalin.captarencu@tissuegnostics.com
TissueGnostics GmbH, Vienna, Austria
Histopathological whole slide image analysis is the golden standard for diagnosis and prognosis of malignant lesions. We are building up powerful computer-aided tools based on cutting-edge deep learning algorithms to foster biomedical research, expand possibilities of analysis, support decisions of physicians, and improve diagnosis for patients.
Kooperationspartner:
Medical University of Vienna
Histopathological image analysis is essential for diagnosis of malignant lesions. But even for experienced pathologists the diagnosis process is not trivial. Diagnostic concordance between specialists is on average only 75% and there is a lack of pathologists in many parts of the world. These limitations motivate the development of Computer-Aided Diagnosis (CAD) systems based on automated image analysis algorithms. Being a second opinion system, CAD systems shall reduce the workload of specialists, improve the diagnosis efficiency, and contribute to cost reduction. Furthermore, automated image analysis is a big data analysis tool that is of high interest for biomedical researchers.
The company TissueGnostics (TG) develops and offers In-Vitro Diagnostics (IVD)-compliant CAD systems for histological image analysis, which allow for quantification of multiple markers and cellular characteristics in tissues samples on the single cell level.
The Medical University of Vienna has a special focus on precision medicine and big data analysis. I. Ellinger and G. Dorffner are interested in using and further developing automated image analysis in their ongoing and future research projects with the ultimate aim to identify new biomarkers, better understand specific diseases and advance personalized medicine.
The partners collaborate in EU- and national funded research projects and develop deep learning-based algorithms for image classification and segmentation.
