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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/9173
Title: Deep Learning in Big Image Data: Histology IMage Classification for Breast Cancer Diagnosis
Authors: Kovalev, V.
Kalinovsky, А.
Liauchuk, V.
Keywords: материалы конференций
Issue Date: Jun-2016
Publisher: БГУИР
Citation: Kovalev, V. Deep Learning in Big Image Data: Histology IMage Classification for Breast Cancer Diagnosis / V. Kovalev, A. Kalinovsky, V. Liauchuk. // BIG DATA and Advanced Analytics. Использование BIG DATA для оптимизации бизнеса и информационных технологий : сборник материалов II международной научно-практической конференции; Минск, 15-17 июня 2016 г. / редкол. : М. П. Батура [и др.]. – Минск : БГУИР, 2016. – С. 44-53
Abstract: This paper present results of the use of Deep Learning approach and Convolutional Neural Networks (CNN) for the problem of breast cancer diagnosis. Specifically, the main goal of this particular study was to detect and to segment (i.e. delineate) regions of micro- and macro- metastases in whole-slide images of lymph node sections. The whole-slide imaging of tissue probes produces very large histological images. The size of resultant color RGB images typically ranges between 50 000х50 000 and 200 000x200 000 pixels and they considered as a basic component of computerized methods in recent Digital Pathology. Original hematoxylin and eosin stained whole- slide images produced by two different optical microscope scanners were kindly provided by founders of CAMELYON16 world-wide competition.
URI: http://libeldoc.bsuir.by/handle/123456789/9173
https://libeldoc.bsuir.by/handle/123456789/9173
ISBN: 978-985-543-237-2
Appears in Collections:BIG DATA and Advanced Analytics. Использование BIG DATA для оптимизации бизнеса и информационных технологий (2016)

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