Polyp segmentation network with hybrid channel-spatial attention and pyramid global context guided feature fusion
In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Huang, Xiaodong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Synthesis of terraced and spherical MgO nanoparticles using flame metal combustion - Chae, Sukbyung ELSEVIER, 2017transfer abstract, the international journal on imaging and image-archiving in all medical specialties : the official journal of the Computerized Medical Image Society, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:98 ; year:2022 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.compmedimag.2022.102072 |
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| 520 | |a In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. | ||
| 520 | |a In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. | ||
| 650 | 7 | |a Hybrid channel-spatial attention |2 Elsevier | |
| 650 | 7 | |a Feature fusion |2 Elsevier | |
| 650 | 7 | |a Polyp segmentation |2 Elsevier | |
| 650 | 7 | |a Global context-aware pyramid feature extraction |2 Elsevier | |
| 700 | 1 | |a Zhuo, Li |4 oth | |
| 700 | 1 | |a Zhang, Hui |4 oth | |
| 700 | 1 | |a Yang, Yang |4 oth | |
| 700 | 1 | |a Li, Xiaoguang |4 oth | |
| 700 | 1 | |a Zhang, Jing |4 oth | |
| 700 | 1 | |a Wei, Wei |4 oth | |
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10.1016/j.compmedimag.2022.102072 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001781.pica (DE-627)ELV057799636 (ELSEVIER)S0895-6111(22)00045-3 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Huang, Xiaodong verfasserin aut Polyp segmentation network with hybrid channel-spatial attention and pyramid global context guided feature fusion 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. Hybrid channel-spatial attention Elsevier Feature fusion Elsevier Polyp segmentation Elsevier Global context-aware pyramid feature extraction Elsevier Zhuo, Li oth Zhang, Hui oth Yang, Yang oth Li, Xiaoguang oth Zhang, Jing oth Wei, Wei oth Enthalten in Elsevier Science Chae, Sukbyung ELSEVIER Synthesis of terraced and spherical MgO nanoparticles using flame metal combustion 2017transfer abstract the international journal on imaging and image-archiving in all medical specialties : the official journal of the Computerized Medical Image Society Amsterdam [u.a.] (DE-627)ELV015598136 volume:98 year:2022 pages:0 https://doi.org/10.1016/j.compmedimag.2022.102072 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV 42.00 Biologie: Allgemeines VZ AR 98 2022 0 |
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10.1016/j.compmedimag.2022.102072 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001781.pica (DE-627)ELV057799636 (ELSEVIER)S0895-6111(22)00045-3 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Huang, Xiaodong verfasserin aut Polyp segmentation network with hybrid channel-spatial attention and pyramid global context guided feature fusion 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. Hybrid channel-spatial attention Elsevier Feature fusion Elsevier Polyp segmentation Elsevier Global context-aware pyramid feature extraction Elsevier Zhuo, Li oth Zhang, Hui oth Yang, Yang oth Li, Xiaoguang oth Zhang, Jing oth Wei, Wei oth Enthalten in Elsevier Science Chae, Sukbyung ELSEVIER Synthesis of terraced and spherical MgO nanoparticles using flame metal combustion 2017transfer abstract the international journal on imaging and image-archiving in all medical specialties : the official journal of the Computerized Medical Image Society Amsterdam [u.a.] (DE-627)ELV015598136 volume:98 year:2022 pages:0 https://doi.org/10.1016/j.compmedimag.2022.102072 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV 42.00 Biologie: Allgemeines VZ AR 98 2022 0 |
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10.1016/j.compmedimag.2022.102072 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001781.pica (DE-627)ELV057799636 (ELSEVIER)S0895-6111(22)00045-3 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Huang, Xiaodong verfasserin aut Polyp segmentation network with hybrid channel-spatial attention and pyramid global context guided feature fusion 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. Hybrid channel-spatial attention Elsevier Feature fusion Elsevier Polyp segmentation Elsevier Global context-aware pyramid feature extraction Elsevier Zhuo, Li oth Zhang, Hui oth Yang, Yang oth Li, Xiaoguang oth Zhang, Jing oth Wei, Wei oth Enthalten in Elsevier Science Chae, Sukbyung ELSEVIER Synthesis of terraced and spherical MgO nanoparticles using flame metal combustion 2017transfer abstract the international journal on imaging and image-archiving in all medical specialties : the official journal of the Computerized Medical Image Society Amsterdam [u.a.] (DE-627)ELV015598136 volume:98 year:2022 pages:0 https://doi.org/10.1016/j.compmedimag.2022.102072 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV 42.00 Biologie: Allgemeines VZ AR 98 2022 0 |
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10.1016/j.compmedimag.2022.102072 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001781.pica (DE-627)ELV057799636 (ELSEVIER)S0895-6111(22)00045-3 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Huang, Xiaodong verfasserin aut Polyp segmentation network with hybrid channel-spatial attention and pyramid global context guided feature fusion 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. Hybrid channel-spatial attention Elsevier Feature fusion Elsevier Polyp segmentation Elsevier Global context-aware pyramid feature extraction Elsevier Zhuo, Li oth Zhang, Hui oth Yang, Yang oth Li, Xiaoguang oth Zhang, Jing oth Wei, Wei oth Enthalten in Elsevier Science Chae, Sukbyung ELSEVIER Synthesis of terraced and spherical MgO nanoparticles using flame metal combustion 2017transfer abstract the international journal on imaging and image-archiving in all medical specialties : the official journal of the Computerized Medical Image Society Amsterdam [u.a.] (DE-627)ELV015598136 volume:98 year:2022 pages:0 https://doi.org/10.1016/j.compmedimag.2022.102072 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV 42.00 Biologie: Allgemeines VZ AR 98 2022 0 |
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10.1016/j.compmedimag.2022.102072 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001781.pica (DE-627)ELV057799636 (ELSEVIER)S0895-6111(22)00045-3 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Huang, Xiaodong verfasserin aut Polyp segmentation network with hybrid channel-spatial attention and pyramid global context guided feature fusion 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. Hybrid channel-spatial attention Elsevier Feature fusion Elsevier Polyp segmentation Elsevier Global context-aware pyramid feature extraction Elsevier Zhuo, Li oth Zhang, Hui oth Yang, Yang oth Li, Xiaoguang oth Zhang, Jing oth Wei, Wei oth Enthalten in Elsevier Science Chae, Sukbyung ELSEVIER Synthesis of terraced and spherical MgO nanoparticles using flame metal combustion 2017transfer abstract the international journal on imaging and image-archiving in all medical specialties : the official journal of the Computerized Medical Image Society Amsterdam [u.a.] (DE-627)ELV015598136 volume:98 year:2022 pages:0 https://doi.org/10.1016/j.compmedimag.2022.102072 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV 42.00 Biologie: Allgemeines VZ AR 98 2022 0 |
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polyp segmentation network with hybrid channel-spatial attention and pyramid global context guided feature fusion |
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Polyp segmentation network with hybrid channel-spatial attention and pyramid global context guided feature fusion |
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In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. |
| abstractGer |
In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. |
| abstract_unstemmed |
In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an encoder-decoder framework. ResNet50 is adopted as the encoder, and three functional modules are introduced to improve the performance. Firstly, a hybrid channel-spatial attention module is introduced to reweight the encoder features spatially and channel-wise, enhancing the critical features for the segmentation task while suppressing irrelevant ones. Secondly, a global context pyramid feature extraction module and a series of global context flows are proposed to extract and deliver the global context information. The former captures the multi-scale and multi-receptive-field global context information, while the latter explicitly transmits the global context information to each decoder level. Finally, a feature fusion module is designed to effectively incorporate the high-level features, low-level features, and global context information, considering the gaps between different features. These modules help the model fully exploit the global context information to deduce the complete polyp regions. Extensive experiments are conducted on five public colorectal polyp datasets. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-art methods. |
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Zhuo, Li Zhang, Hui Yang, Yang Li, Xiaoguang Zhang, Jing Wei, Wei |
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