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Title:

Quantifying Heterogeneity in Tumors: Proposing a New Method Utilizing Convolutional Neuronal Networks.

Document type:
Journal Article
Author(s):
Prokop, Georg; Örtl, Michael; Fotteler, Marina; Schüffler, Peter; Schobel, Johannes; Swoboda, Walter; Schlegel, Jürgen; Liesche-Starnecker, Friederike
Abstract:
Heterogeneity is a hallmark of glioblastoma (GBM), the most common malignant brain tumor, and a key reason for the poor survival rate of patients. However, establishing a clinically applicable, cost-efficient tool to measure and quantify heterogeneity is challenging. We present a novel method in an ongoing study utilizing two convolutional neuronal networks (CNN). After digitizing tumor samples, the first CNN delimitates GBM from normal tissue, the second quantifies heterogeneity within the tumo...     »
Journal title abbreviation:
Stud Health Technol Inform
Year:
2022
Journal volume:
289
Pages contribution:
397-400
Fulltext / DOI:
doi:10.3233/SHTI210942
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/35062175
Print-ISSN:
0926-9630
TUM Institution:
Institut für Allgemeine Pathologie und Pathologische Anatomie
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