CI-UNet: melding convnext and cross-dimensional attention for robust medical image segmentation.
Biomed Eng Lett
; 14(2): 341-353, 2024 Mar.
Article
em En
| MEDLINE
| ID: mdl-38374903
ABSTRACT
Deep learning-based methods have recently shown great promise in medical image segmentation task. However, CNN-based frameworks struggle with inadequate long-range spatial dependency capture, whereas Transformers suffer from computational inefficiency and necessitate substantial volumes of labeled data for effective training. To tackle these issues, this paper introduces CI-UNet, a novel architecture that utilizes ConvNeXt as its encoder, amalgamating the computational efficiency and feature extraction capabilities. Moreover, an advanced attention mechanism is proposed to captures intricate cross-dimensional interactions and global context. Extensive experiments on two segmentation datasets, namely BCSD, and CT2USforKidneySeg, confirm the excellent performance of the proposed CI-UNet as compared to other segmentation methods.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Biomed Eng Lett
Ano de publicação:
2024
Tipo de documento:
Article
País de publicação:
Alemanha