Cross-modal tumor segmentation using generative blending augmentation and self training

\textit{Objectives}: Data scarcity and domain shifts lead to biased training sets that do not accurately represent deployment conditions. A related practical problem is cross-modal image segmentation, where the objective is to segment unlabelled images using previously labelled datasets from other i...

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Veröffentlicht in:	arXiv.org 2024-03
Hauptverfasser:	Sallé, Guillaume, Pierre-Henri Conze, Julien, Bert, Boussion, Nicolas, Visvikis, Dimitris, Jaouen, Vincent
Format:	Artikel
Sprache:	eng
Schlagworte:	Blending Context Data augmentation Domains Image segmentation Iterative methods Machine learning Medical imaging Testing time Training Tumors
Online-Zugang:	Volltext
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