PuSH - Publikationsserver des Helmholtz Zentrums München: Risk score classification of renal masses on CT imaging data using a convolutional neural network.

Informationen

Hinweise zu Qualitätskriterien von Zeitschriften

Open-Access-Richtlinie der Helmholtz-Gemeinschaft 2016

CC Licencences

Metriken für Publikationen

Navigation

Startseite

English

EVA: Veröffentlichen

Neuer EVA Antrag

Recherche

Erweiterte Suche

Durchblättern nach ...

... HMGU-Autoren/Konsortien

... Organisationsstruktur

... Zeitschriften

... Publikationstypen

... Forschungsdaten

... Arbeitsgruppen

... Erscheinungsjahr

Publikationen im Überblick

Statistik

HGF Fortschrittsbericht

OA Publikationen

Eintragen

Neue Publikation eintragen

Neue Publikation holen aus...

...EVA

Fehlende Publikation melden

Highlights

Suche

Hilfe & Kontakt

Ansprechpartner

Hilfe

Datenschutz

Helmholtz Open Science

Bibliometrische Indikatoren

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

Lang, D.M. ; Peeken, J.C. ; Combs, S.E. ; Wilkens, J.J. ; Bartzsch, S.

Risk score classification of renal masses on CT imaging data using a convolutional neural network.

ISBIC Proceedings (2022)

DOI

Abstract
Metriken
Zusatzinfos

The variety of treatment options for clinically localized renal masses is diverse. Medical imaging depicts a non-invasive technique able to retrieve detailed information that can be utilized for treatment decision. For the ISBI KNIGHT challenge, we studied the ability of deep neural networks for renal mass risk score prediction based on CT imaging and clinical features. A U-Net archi-tecture was trained for segmentation of the region of interest. Afterwards, patches of both kidneys were input into the convolutional layers of a neural network, resulting feature vectors were fused using the element-wise maximum and clinical features were merged with deep features for risk score classification. The model achieved an area under the receiver operating characteristics curve (AUC) of 0.814 on the test set for dis-crimination of clinical relevant subclasses. For the more de-tailed risk score prediction task a mean AUC of 0.676 was achieved.

Impact Factor

Scopus SNIP

Altmetric

2.988

0.000