DMFC-UFormer: Depthwise multi-scale factorized convolution transformer-based UNet for medical image segmentation

Medical image segmentation provides a crucial foundation for cancer diagnosis. Transformers are adept at understanding global context and complex dependencies. CNNs, meanwhile, are efficient for local feature extraction and hierarchical learning but struggle with long-range dependencies. In this pap...

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Veröffentlicht in:	Biomedical signal processing and control Jg. 101; S. 107200
Hauptverfasser:	Garbaz, Anass, Oukdach, Yassine, Charfi, Said, Ansari, Mohamed El, Koutti, Lahcen, Salihoun, Mouna
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.03.2025
Schlagworte:	DMFC-UFormer Local–global contextual information Medical image segmentation Multi-scale factorized convolution Transformer UNet Multi-scale factorized convolution Transformer DMFC-UFormer Local–global contextual information Medical image segmentation UNet
ISSN:	1746-8094
Online-Zugang:	Volltext
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