Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation

This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without shari...

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Veröffentlicht in:	Radiotherapy and oncology 2024-09, Vol.198, p.110419, Article 110419
Hauptverfasser:	Huang, Yixing, Khodabakhshi, Zahra, Gomaa, Ahmed, Schmidt, Manuel, Fietkau, Rainer, Guckenberger, Matthias, Andratschke, Nicolaus, Bert, Christoph, Tanadini-Lang, Stephanie, Putz, Florian
Format:	Artikel
Sprache:	eng
Schlagworte:	Brain metastasis Continual learning Data heterogeneity Data privacy Learning without forgetting Multicenter collaboration
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