A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet

Semantic Segmentation is an extremely important medical image auxiliary analysis method. However, existing networks have the following problems: 1) The amount of feature information of Encoder and Decoder is not equal under multi-branch architecture; 2) The direct processing of the original image by...
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Gespeichert in:

Autor*in:	Wang, Yuefei [verfasserIn] Yu, Xi [verfasserIn] Guo, Xiaoyan [verfasserIn] Wang, Xilei [verfasserIn] Wei, Yuanhong [verfasserIn] Zeng, Shijie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Semantic Segmentation U-Shaped Network Transformer ViT Medical Image

Übergeordnetes Werk:	Enthalten in: Journal of visual communication and image representation - Orlando, Fla. : Academic Press, 1990, 95
Übergeordnetes Werk:	volume:95

DOI / URN:	10.1016/j.jvcir.2023.103856

Katalog-ID:	ELV061767794

Internformat


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520			\|a Semantic Segmentation is an extremely important medical image auxiliary analysis method. However, existing networks have the following problems: 1) The amount of feature information of Encoder and Decoder is not equal under multi-branch architecture; 2) The direct processing of the original image by ViT Encoder is not sufficient; 3) Multi-channel features are too independent and lack of fusion. Combined with the ViT Encoder framework, this study proposes a 'Single Encoder – Double Decoder' structure: DBUNet. Firstly, ViT Encoder is employed as a part of the Decoder branches to enhance the shallow features. Then, a polarization amplification of channel weights is proposed and placed in front of the ViT Encoder module to achieve early image processing. Finally, a Bottleneck for feature fusion is proposed to solve the problem of channel independence. The comprehensive verification of 13 comparative networks in three aspects, combined with ablation experiments, jointly proves the superiority of DBUNet.
650		4	\|a Semantic Segmentation
650		4	\|a U-Shaped Network
650		4	\|a Transformer ViT
650		4	\|a Medical Image
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700	1		\|a Guo, Xiaoyan \|e verfasserin \|4 aut
700	1		\|a Wang, Xilei \|e verfasserin \|4 aut
700	1		\|a Wei, Yuanhong \|e verfasserin \|4 aut
700	1		\|a Zeng, Shijie \|e verfasserin \|4 aut
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912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
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936	b	k	\|a 54.74 \|j Maschinelles Sehen \|q VZ
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publishDate	2023
allfields	10.1016/j.jvcir.2023.103856 doi (DE-627)ELV061767794 (ELSEVIER)S1047-3203(23)00106-2 DE-627 ger DE-627 rda eng 620 VZ 54.74 bkl Wang, Yuefei verfasserin (orcid)0000-0003-3032-1852 aut A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Semantic Segmentation is an extremely important medical image auxiliary analysis method. However, existing networks have the following problems: 1) The amount of feature information of Encoder and Decoder is not equal under multi-branch architecture; 2) The direct processing of the original image by ViT Encoder is not sufficient; 3) Multi-channel features are too independent and lack of fusion. Combined with the ViT Encoder framework, this study proposes a 'Single Encoder – Double Decoder' structure: DBUNet. Firstly, ViT Encoder is employed as a part of the Decoder branches to enhance the shallow features. Then, a polarization amplification of channel weights is proposed and placed in front of the ViT Encoder module to achieve early image processing. Finally, a Bottleneck for feature fusion is proposed to solve the problem of channel independence. The comprehensive verification of 13 comparative networks in three aspects, combined with ablation experiments, jointly proves the superiority of DBUNet. Semantic Segmentation U-Shaped Network Transformer ViT Medical Image Yu, Xi verfasserin aut Guo, Xiaoyan verfasserin aut Wang, Xilei verfasserin aut Wei, Yuanhong verfasserin aut Zeng, Shijie verfasserin aut Enthalten in Journal of visual communication and image representation Orlando, Fla. : Academic Press, 1990 95 Online-Ressource (DE-627)267838247 (DE-600)1470957-0 (DE-576)114818010 1047-3203 nnns volume:95 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 95
spelling	10.1016/j.jvcir.2023.103856 doi (DE-627)ELV061767794 (ELSEVIER)S1047-3203(23)00106-2 DE-627 ger DE-627 rda eng 620 VZ 54.74 bkl Wang, Yuefei verfasserin (orcid)0000-0003-3032-1852 aut A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Semantic Segmentation is an extremely important medical image auxiliary analysis method. However, existing networks have the following problems: 1) The amount of feature information of Encoder and Decoder is not equal under multi-branch architecture; 2) The direct processing of the original image by ViT Encoder is not sufficient; 3) Multi-channel features are too independent and lack of fusion. Combined with the ViT Encoder framework, this study proposes a 'Single Encoder – Double Decoder' structure: DBUNet. Firstly, ViT Encoder is employed as a part of the Decoder branches to enhance the shallow features. Then, a polarization amplification of channel weights is proposed and placed in front of the ViT Encoder module to achieve early image processing. Finally, a Bottleneck for feature fusion is proposed to solve the problem of channel independence. The comprehensive verification of 13 comparative networks in three aspects, combined with ablation experiments, jointly proves the superiority of DBUNet. Semantic Segmentation U-Shaped Network Transformer ViT Medical Image Yu, Xi verfasserin aut Guo, Xiaoyan verfasserin aut Wang, Xilei verfasserin aut Wei, Yuanhong verfasserin aut Zeng, Shijie verfasserin aut Enthalten in Journal of visual communication and image representation Orlando, Fla. : Academic Press, 1990 95 Online-Ressource (DE-627)267838247 (DE-600)1470957-0 (DE-576)114818010 1047-3203 nnns volume:95 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 95
allfields_unstemmed	10.1016/j.jvcir.2023.103856 doi (DE-627)ELV061767794 (ELSEVIER)S1047-3203(23)00106-2 DE-627 ger DE-627 rda eng 620 VZ 54.74 bkl Wang, Yuefei verfasserin (orcid)0000-0003-3032-1852 aut A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Semantic Segmentation is an extremely important medical image auxiliary analysis method. However, existing networks have the following problems: 1) The amount of feature information of Encoder and Decoder is not equal under multi-branch architecture; 2) The direct processing of the original image by ViT Encoder is not sufficient; 3) Multi-channel features are too independent and lack of fusion. Combined with the ViT Encoder framework, this study proposes a 'Single Encoder – Double Decoder' structure: DBUNet. Firstly, ViT Encoder is employed as a part of the Decoder branches to enhance the shallow features. Then, a polarization amplification of channel weights is proposed and placed in front of the ViT Encoder module to achieve early image processing. Finally, a Bottleneck for feature fusion is proposed to solve the problem of channel independence. The comprehensive verification of 13 comparative networks in three aspects, combined with ablation experiments, jointly proves the superiority of DBUNet. Semantic Segmentation U-Shaped Network Transformer ViT Medical Image Yu, Xi verfasserin aut Guo, Xiaoyan verfasserin aut Wang, Xilei verfasserin aut Wei, Yuanhong verfasserin aut Zeng, Shijie verfasserin aut Enthalten in Journal of visual communication and image representation Orlando, Fla. : Academic Press, 1990 95 Online-Ressource (DE-627)267838247 (DE-600)1470957-0 (DE-576)114818010 1047-3203 nnns volume:95 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 95
allfieldsGer	10.1016/j.jvcir.2023.103856 doi (DE-627)ELV061767794 (ELSEVIER)S1047-3203(23)00106-2 DE-627 ger DE-627 rda eng 620 VZ 54.74 bkl Wang, Yuefei verfasserin (orcid)0000-0003-3032-1852 aut A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Semantic Segmentation is an extremely important medical image auxiliary analysis method. However, existing networks have the following problems: 1) The amount of feature information of Encoder and Decoder is not equal under multi-branch architecture; 2) The direct processing of the original image by ViT Encoder is not sufficient; 3) Multi-channel features are too independent and lack of fusion. Combined with the ViT Encoder framework, this study proposes a 'Single Encoder – Double Decoder' structure: DBUNet. Firstly, ViT Encoder is employed as a part of the Decoder branches to enhance the shallow features. Then, a polarization amplification of channel weights is proposed and placed in front of the ViT Encoder module to achieve early image processing. Finally, a Bottleneck for feature fusion is proposed to solve the problem of channel independence. The comprehensive verification of 13 comparative networks in three aspects, combined with ablation experiments, jointly proves the superiority of DBUNet. Semantic Segmentation U-Shaped Network Transformer ViT Medical Image Yu, Xi verfasserin aut Guo, Xiaoyan verfasserin aut Wang, Xilei verfasserin aut Wei, Yuanhong verfasserin aut Zeng, Shijie verfasserin aut Enthalten in Journal of visual communication and image representation Orlando, Fla. : Academic Press, 1990 95 Online-Ressource (DE-627)267838247 (DE-600)1470957-0 (DE-576)114818010 1047-3203 nnns volume:95 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 95
allfieldsSound	10.1016/j.jvcir.2023.103856 doi (DE-627)ELV061767794 (ELSEVIER)S1047-3203(23)00106-2 DE-627 ger DE-627 rda eng 620 VZ 54.74 bkl Wang, Yuefei verfasserin (orcid)0000-0003-3032-1852 aut A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Semantic Segmentation is an extremely important medical image auxiliary analysis method. However, existing networks have the following problems: 1) The amount of feature information of Encoder and Decoder is not equal under multi-branch architecture; 2) The direct processing of the original image by ViT Encoder is not sufficient; 3) Multi-channel features are too independent and lack of fusion. Combined with the ViT Encoder framework, this study proposes a 'Single Encoder – Double Decoder' structure: DBUNet. Firstly, ViT Encoder is employed as a part of the Decoder branches to enhance the shallow features. Then, a polarization amplification of channel weights is proposed and placed in front of the ViT Encoder module to achieve early image processing. Finally, a Bottleneck for feature fusion is proposed to solve the problem of channel independence. The comprehensive verification of 13 comparative networks in three aspects, combined with ablation experiments, jointly proves the superiority of DBUNet. Semantic Segmentation U-Shaped Network Transformer ViT Medical Image Yu, Xi verfasserin aut Guo, Xiaoyan verfasserin aut Wang, Xilei verfasserin aut Wei, Yuanhong verfasserin aut Zeng, Shijie verfasserin aut Enthalten in Journal of visual communication and image representation Orlando, Fla. : Academic Press, 1990 95 Online-Ressource (DE-627)267838247 (DE-600)1470957-0 (DE-576)114818010 1047-3203 nnns volume:95 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 Maschinelles Sehen VZ AR 95
language	English
source	Enthalten in Journal of visual communication and image representation 95 volume:95
sourceStr	Enthalten in Journal of visual communication and image representation 95 volume:95
format_phy_str_mv	Article
bklname	Maschinelles Sehen
institution	findex.gbv.de
topic_facet	Semantic Segmentation U-Shaped Network Transformer ViT Medical Image
dewey-raw	620
isfreeaccess_bool	false
container_title	Journal of visual communication and image representation
authorswithroles_txt_mv	Wang, Yuefei @@aut@@ Yu, Xi @@aut@@ Guo, Xiaoyan @@aut@@ Wang, Xilei @@aut@@ Wei, Yuanhong @@aut@@ Zeng, Shijie @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	267838247
dewey-sort	3620
id	ELV061767794
language_de	englisch
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author	Wang, Yuefei
spellingShingle	Wang, Yuefei ddc 620 bkl 54.74 misc Semantic Segmentation misc U-Shaped Network misc Transformer ViT misc Medical Image A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet
authorStr	Wang, Yuefei
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topic_title	620 VZ 54.74 bkl A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet Semantic Segmentation U-Shaped Network Transformer ViT Medical Image
topic	ddc 620 bkl 54.74 misc Semantic Segmentation misc U-Shaped Network misc Transformer ViT misc Medical Image
topic_unstemmed	ddc 620 bkl 54.74 misc Semantic Segmentation misc U-Shaped Network misc Transformer ViT misc Medical Image
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title	A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet
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title_full	A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet
author_sort	Wang, Yuefei
journal	Journal of visual communication and image representation
journalStr	Journal of visual communication and image representation
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author_browse	Wang, Yuefei Yu, Xi Guo, Xiaoyan Wang, Xilei Wei, Yuanhong Zeng, Shijie
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author-letter	Wang, Yuefei
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title_sort	a dual-decoding branch u-shaped semantic segmentation network combining transformer attention with decoder: dbunet
title_auth	A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet
abstract	Semantic Segmentation is an extremely important medical image auxiliary analysis method. However, existing networks have the following problems: 1) The amount of feature information of Encoder and Decoder is not equal under multi-branch architecture; 2) The direct processing of the original image by ViT Encoder is not sufficient; 3) Multi-channel features are too independent and lack of fusion. Combined with the ViT Encoder framework, this study proposes a 'Single Encoder – Double Decoder' structure: DBUNet. Firstly, ViT Encoder is employed as a part of the Decoder branches to enhance the shallow features. Then, a polarization amplification of channel weights is proposed and placed in front of the ViT Encoder module to achieve early image processing. Finally, a Bottleneck for feature fusion is proposed to solve the problem of channel independence. The comprehensive verification of 13 comparative networks in three aspects, combined with ablation experiments, jointly proves the superiority of DBUNet.
abstractGer	Semantic Segmentation is an extremely important medical image auxiliary analysis method. However, existing networks have the following problems: 1) The amount of feature information of Encoder and Decoder is not equal under multi-branch architecture; 2) The direct processing of the original image by ViT Encoder is not sufficient; 3) Multi-channel features are too independent and lack of fusion. Combined with the ViT Encoder framework, this study proposes a 'Single Encoder – Double Decoder' structure: DBUNet. Firstly, ViT Encoder is employed as a part of the Decoder branches to enhance the shallow features. Then, a polarization amplification of channel weights is proposed and placed in front of the ViT Encoder module to achieve early image processing. Finally, a Bottleneck for feature fusion is proposed to solve the problem of channel independence. The comprehensive verification of 13 comparative networks in three aspects, combined with ablation experiments, jointly proves the superiority of DBUNet.
abstract_unstemmed	Semantic Segmentation is an extremely important medical image auxiliary analysis method. However, existing networks have the following problems: 1) The amount of feature information of Encoder and Decoder is not equal under multi-branch architecture; 2) The direct processing of the original image by ViT Encoder is not sufficient; 3) Multi-channel features are too independent and lack of fusion. Combined with the ViT Encoder framework, this study proposes a 'Single Encoder – Double Decoder' structure: DBUNet. Firstly, ViT Encoder is employed as a part of the Decoder branches to enhance the shallow features. Then, a polarization amplification of channel weights is proposed and placed in front of the ViT Encoder module to achieve early image processing. Finally, a Bottleneck for feature fusion is proposed to solve the problem of channel independence. The comprehensive verification of 13 comparative networks in three aspects, combined with ablation experiments, jointly proves the superiority of DBUNet.
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title_short	A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet
remote_bool	true
author2	Yu, Xi Guo, Xiaoyan Wang, Xilei Wei, Yuanhong Zeng, Shijie
author2Str	Yu, Xi Guo, Xiaoyan Wang, Xilei Wei, Yuanhong Zeng, Shijie
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doi_str	10.1016/j.jvcir.2023.103856
up_date	2024-07-06T18:01:56.943Z
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A Dual-Decoding branch U-shaped semantic segmentation network combining Transformer attention with Decoder: DBUNet

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