CFHA-Net: A polyp segmentation method with cross-scale fusion strategy and hybrid attention
Authors: Yang, L., Zhai, C., Liu, Y. and Yu, H.
Journal: Computers in Biology and Medicine
Volume: 164
eISSN: 1879-0534
ISSN: 0010-4825
DOI: 10.1016/j.compbiomed.2023.107301
Abstract:Colorectal cancer is a prevalent disease in modern times, with most cases being caused by polyps. Therefore, the segmentation of polyps has garnered significant attention in the field of medical image segmentation. In recent years, the variant network derived from the U-Net network has demonstrated a good segmentation effect on polyp segmentation challenges. In this paper, a polyp segmentation model, called CFHA-Net, is proposed, that combines a cross-scale feature fusion strategy and a hybrid attention mechanism. Inspired by feature learning, the encoder unit incorporates a cross-scale context fusion (CCF) module that performs cross-layer feature fusion and enhances the feature information of different scales. The skip connection is optimized by proposed triple hybrid attention (THA) module that aggregates spatial and channel attention features from three directions to improve the long-range dependence between features and help identify subsequent polyp lesion boundaries. Additionally, a dense-receptive feature fusion (DFF) module, which combines dense connections and multi-receptive field fusion modules, is added at the bottleneck layer to capture more comprehensive context information. Furthermore, a hybrid pooling (HP) module and a hybrid upsampling (HU) module are proposed to help the segmentation network acquire more contextual features. A series of experiments have been conducted on three typical datasets for polyp segmentation (CVC-ClinicDB, Kvasir-SEG, EndoTect) to evaluate the effectiveness and generalization of the proposed CFHA-Net. The experimental results demonstrate the validity and generalization of the proposed method, with many performance metrics surpassing those of related advanced segmentation networks. Therefore, proposed CFHA-Net could present a promising solution to the challenges of polyp segmentation in medical image analysis. The source code of proposed CFHA-Net is available at https://github.com/CXzhai/CFHA-Net.git.
Source: Scopus
CFHA-Net: A polyp segmentation method with cross-scale fusion strategy and hybrid attention.
Authors: Yang, L., Zhai, C., Liu, Y. and Yu, H.
Journal: Comput Biol Med
Volume: 164
Pages: 107301
eISSN: 1879-0534
DOI: 10.1016/j.compbiomed.2023.107301
Abstract:Colorectal cancer is a prevalent disease in modern times, with most cases being caused by polyps. Therefore, the segmentation of polyps has garnered significant attention in the field of medical image segmentation. In recent years, the variant network derived from the U-Net network has demonstrated a good segmentation effect on polyp segmentation challenges. In this paper, a polyp segmentation model, called CFHA-Net, is proposed, that combines a cross-scale feature fusion strategy and a hybrid attention mechanism. Inspired by feature learning, the encoder unit incorporates a cross-scale context fusion (CCF) module that performs cross-layer feature fusion and enhances the feature information of different scales. The skip connection is optimized by proposed triple hybrid attention (THA) module that aggregates spatial and channel attention features from three directions to improve the long-range dependence between features and help identify subsequent polyp lesion boundaries. Additionally, a dense-receptive feature fusion (DFF) module, which combines dense connections and multi-receptive field fusion modules, is added at the bottleneck layer to capture more comprehensive context information. Furthermore, a hybrid pooling (HP) module and a hybrid upsampling (HU) module are proposed to help the segmentation network acquire more contextual features. A series of experiments have been conducted on three typical datasets for polyp segmentation (CVC-ClinicDB, Kvasir-SEG, EndoTect) to evaluate the effectiveness and generalization of the proposed CFHA-Net. The experimental results demonstrate the validity and generalization of the proposed method, with many performance metrics surpassing those of related advanced segmentation networks. Therefore, proposed CFHA-Net could present a promising solution to the challenges of polyp segmentation in medical image analysis. The source code of proposed CFHA-Net is available at https://github.com/CXzhai/CFHA-Net.git.
Source: PubMed
CFHA-Net: A polyp segmentation method with cross-scale fusion strategy and hybrid attention.
Authors: Yang, L., Zhai, C., Liu, Y. and Yu, H.
Journal: Computers in biology and medicine
Volume: 164
Pages: 107301
eISSN: 1879-0534
ISSN: 0010-4825
DOI: 10.1016/j.compbiomed.2023.107301
Abstract:Colorectal cancer is a prevalent disease in modern times, with most cases being caused by polyps. Therefore, the segmentation of polyps has garnered significant attention in the field of medical image segmentation. In recent years, the variant network derived from the U-Net network has demonstrated a good segmentation effect on polyp segmentation challenges. In this paper, a polyp segmentation model, called CFHA-Net, is proposed, that combines a cross-scale feature fusion strategy and a hybrid attention mechanism. Inspired by feature learning, the encoder unit incorporates a cross-scale context fusion (CCF) module that performs cross-layer feature fusion and enhances the feature information of different scales. The skip connection is optimized by proposed triple hybrid attention (THA) module that aggregates spatial and channel attention features from three directions to improve the long-range dependence between features and help identify subsequent polyp lesion boundaries. Additionally, a dense-receptive feature fusion (DFF) module, which combines dense connections and multi-receptive field fusion modules, is added at the bottleneck layer to capture more comprehensive context information. Furthermore, a hybrid pooling (HP) module and a hybrid upsampling (HU) module are proposed to help the segmentation network acquire more contextual features. A series of experiments have been conducted on three typical datasets for polyp segmentation (CVC-ClinicDB, Kvasir-SEG, EndoTect) to evaluate the effectiveness and generalization of the proposed CFHA-Net. The experimental results demonstrate the validity and generalization of the proposed method, with many performance metrics surpassing those of related advanced segmentation networks. Therefore, proposed CFHA-Net could present a promising solution to the challenges of polyp segmentation in medical image analysis. The source code of proposed CFHA-Net is available at https://github.com/CXzhai/CFHA-Net.git.
Source: Europe PubMed Central