DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images

Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and its clinical outcomes. Still, there are no highly accurate automated methods to correlate histolopathological images with brain cancer patients’...

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Veröffentlicht in:	Medical & biological engineering & computing 2020-05, Vol.58 (5), p.1031-1045
Hauptverfasser:	Zadeh Shirazi, Amin, Fornaciari, Eric, Bagherian, Narjes Sadat, Ebert, Lisa M., Koszyca, Barbara, Gomez, Guillermo A.
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial intelligence Artificial neural networks Automation Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Brain Brain cancer Brain Neoplasms - mortality Brain Neoplasms - pathology Cancer Classification Classifiers Computer Applications Datasets Deep Learning Genomes Histocytochemistry Human Physiology Humans Image classification Image Interpretation, Computer-Assisted - methods Imaging Medical imaging Mutation Neural networks Neural Networks, Computer Original Original Article Patients Radiology Survival Survival Analysis
Online-Zugang:	Volltext
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