Deep Learning for Automated Histological Image Classification

Histopathologic image analysis is the golden standard for diagnosis of malignant lesions, but manual examination of images causes intense workload for pathologists. Computer-aided diagnosis (CAD) systems shall improve the diagnosis efficiency and increase inter-observer agreement. The TissueGnostics GmbH develop deep learning-based CAD systems to support physicians and improve diagnosis for patients.

Eingereicht von: Rupert Ecker, PhD
Amirreza Mahbod
Robert Nica
Catalin Captarencu
Firma/Universität: TissueGnostics GmbH
Homepage: www.tissuegnostics.com
Kooperationspartner: Medical University Vienna

life-science.eu - Foto: (c) Rupert EckerDigital pathology was introduced several years ago. A very important contribution to exploit the full potential of digital pathology is now the development of computer-aided diagnosis (CAD) algorithms for histopathological image analysis.

CAD will have benefits for pathologists in making routine tasks easier. Furthermore, advanced quantification of histopathological patterns will add valuable information to the diagnostic process. Surgeons and oncologists may also benefit from increased speed and accuracy of diagnosis and prognosis. Finally, and most important, patients will benefit because the diagnostic process may be more accurate and personalised, which is the prerequisite for current efforts towards personalized medicine.

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