MCA-UNet: multi-scale cross co-attentional U-Net for automatic medical image segmentation.
Health Inf Sci Syst
; 11(1): 10, 2023 Dec.
Article
em En
| MEDLINE
| ID: mdl-36721640
ABSTRACT
Medical image segmentation is a challenging task due to the high variation in shape, size and position of infections or lesions in medical images. It is necessary to construct multi-scale representations to capture image contents from different scales. However, it is still challenging for U-Net with a simple skip connection to model the global multi-scale context. To overcome it, we proposed a dense skip-connection with cross co-attention in U-Net to solve the semantic gaps for an accurate automatic medical image segmentation. We name our method MCA-UNet, which enjoys two benefits (1) it has a strong ability to model the multi-scale features, and (2) it jointly explores the spatial and channel attentions. The experimental results on the COVID-19 and IDRiD datasets suggest that our MCA-UNet produces more precise segmentation performance for the consolidation, ground-glass opacity (GGO), microaneurysms (MA) and hard exudates (EX). The source code of this work will be released via https//github.com/McGregorWwww/MCA-UNet/.
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Base de dados:
MEDLINE
Idioma:
En
Revista:
Health Inf Sci Syst
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China