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Titel:

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

Dokumenttyp:
Journal Article
Autor(en):
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...     »
Zeitschriftentitel:
Stud Health Technol Inform
Jahr:
2022
Band / Volume:
289
Seitenangaben Beitrag:
397-400
Volltext / DOI:
doi:10.3233/SHTI210942
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/35062175
Print-ISSN:
0926-9630
TUM Einrichtung:
Institut für Allgemeine Pathologie und Pathologische Anatomie
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