A novel encoder–decoder wavelet model for multifocal region segmentation of TAO facial images
Abstract Thyroid-associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on patients` life and health of. Clinically, the Clinical Activity Score (CAS) is one of the crucial methods for the early diagnosis of TAO. However, due to the diversity of TAO sym...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhu, Haipeng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
Multi-scale cascaded attention mechanism |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - London : Springer, 1993, 35(2023), 26 vom: 20. Juni, Seite 19145-19167 |
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| Übergeordnetes Werk: |
volume:35 ; year:2023 ; number:26 ; day:20 ; month:06 ; pages:19145-19167 |
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| DOI / URN: |
10.1007/s00521-023-08727-2 |
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SPR05273661X |
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| 520 | |a Abstract Thyroid-associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on patients` life and health of. Clinically, the Clinical Activity Score (CAS) is one of the crucial methods for the early diagnosis of TAO. However, due to the diversity of TAO symptoms, utilizing medical expertise to artificially obtain CAS scores is challenging and highly dependent on personal subjectivity. Therefore, accurate identification of TAO regions segmented by scientific techniques is one of the essential prerequisites for the objective acquisition of the CAS scores. In this study, an encoder–decoder wavelet model (EDWM) with multiple-scale cascaded attention mechanism (MCAM) and residual deformable convolution (RDC) was proposed for multifocal region segmentation of TAO from facial images. The proposed method employs the discrete wavelet transform (DWT) to construct an encoder structure for the coarse feature extraction of the diseased regions. The inverse wavelet transform (IWT) is designed to build a decoder structure for resolution recovery. Meanwhile, the MCAM is developed to extract finer features of adjacent wavelet scales in the encoder structure by suppressing the background and focusing on the coarse segmentation of the diseased regions. The RDC is ultimately utilized for enlargement of arbitrary receptive fields and the accurate multi-segmentation task in different regions. In comparison with other selected benchmark models, the EDWM has, respectively, achieved state-of-the-art segmentation performance with 93.12% and 0.804 of the precision and the MIoU when tested on the images of 600 TAO patients. Since the EDWM is characterized by compact structure, interpretability, and strong feature extraction capability, it can provide a much more reliable and scientific basis for the early detection and diagnosis of TAO, reducing reliance on subjective experience in obtaining CAS scores. | ||
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| 700 | 1 | |a Li, Kunhao |4 aut | |
| 700 | 1 | |a Zhou, Lei |4 aut | |
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10.1007/s00521-023-08727-2 doi (DE-627)SPR05273661X (SPR)s00521-023-08727-2-e DE-627 ger DE-627 rakwb eng Zhu, Haipeng verfasserin aut A novel encoder–decoder wavelet model for multifocal region segmentation of TAO facial images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Thyroid-associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on patients` life and health of. Clinically, the Clinical Activity Score (CAS) is one of the crucial methods for the early diagnosis of TAO. However, due to the diversity of TAO symptoms, utilizing medical expertise to artificially obtain CAS scores is challenging and highly dependent on personal subjectivity. Therefore, accurate identification of TAO regions segmented by scientific techniques is one of the essential prerequisites for the objective acquisition of the CAS scores. In this study, an encoder–decoder wavelet model (EDWM) with multiple-scale cascaded attention mechanism (MCAM) and residual deformable convolution (RDC) was proposed for multifocal region segmentation of TAO from facial images. The proposed method employs the discrete wavelet transform (DWT) to construct an encoder structure for the coarse feature extraction of the diseased regions. The inverse wavelet transform (IWT) is designed to build a decoder structure for resolution recovery. Meanwhile, the MCAM is developed to extract finer features of adjacent wavelet scales in the encoder structure by suppressing the background and focusing on the coarse segmentation of the diseased regions. The RDC is ultimately utilized for enlargement of arbitrary receptive fields and the accurate multi-segmentation task in different regions. In comparison with other selected benchmark models, the EDWM has, respectively, achieved state-of-the-art segmentation performance with 93.12% and 0.804 of the precision and the MIoU when tested on the images of 600 TAO patients. Since the EDWM is characterized by compact structure, interpretability, and strong feature extraction capability, it can provide a much more reliable and scientific basis for the early detection and diagnosis of TAO, reducing reliance on subjective experience in obtaining CAS scores. TAO (dpeaa)DE-He213 Wavelet transform (dpeaa)DE-He213 Multi-scale cascaded attention mechanism (dpeaa)DE-He213 Residual deformable convolution (dpeaa)DE-He213 Facial image segmentation (dpeaa)DE-He213 Zhou, Huifang aut He, Hong (orcid)0000-0002-2584-2891 aut Chen, Jiayu aut Song, Xuefei aut Li, Kunhao aut Zhou, Lei aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 26 vom: 20. Juni, Seite 19145-19167 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:26 day:20 month:06 pages:19145-19167 https://dx.doi.org/10.1007/s00521-023-08727-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 26 20 06 19145-19167 |
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10.1007/s00521-023-08727-2 doi (DE-627)SPR05273661X (SPR)s00521-023-08727-2-e DE-627 ger DE-627 rakwb eng Zhu, Haipeng verfasserin aut A novel encoder–decoder wavelet model for multifocal region segmentation of TAO facial images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Thyroid-associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on patients` life and health of. Clinically, the Clinical Activity Score (CAS) is one of the crucial methods for the early diagnosis of TAO. However, due to the diversity of TAO symptoms, utilizing medical expertise to artificially obtain CAS scores is challenging and highly dependent on personal subjectivity. Therefore, accurate identification of TAO regions segmented by scientific techniques is one of the essential prerequisites for the objective acquisition of the CAS scores. In this study, an encoder–decoder wavelet model (EDWM) with multiple-scale cascaded attention mechanism (MCAM) and residual deformable convolution (RDC) was proposed for multifocal region segmentation of TAO from facial images. The proposed method employs the discrete wavelet transform (DWT) to construct an encoder structure for the coarse feature extraction of the diseased regions. The inverse wavelet transform (IWT) is designed to build a decoder structure for resolution recovery. Meanwhile, the MCAM is developed to extract finer features of adjacent wavelet scales in the encoder structure by suppressing the background and focusing on the coarse segmentation of the diseased regions. The RDC is ultimately utilized for enlargement of arbitrary receptive fields and the accurate multi-segmentation task in different regions. In comparison with other selected benchmark models, the EDWM has, respectively, achieved state-of-the-art segmentation performance with 93.12% and 0.804 of the precision and the MIoU when tested on the images of 600 TAO patients. Since the EDWM is characterized by compact structure, interpretability, and strong feature extraction capability, it can provide a much more reliable and scientific basis for the early detection and diagnosis of TAO, reducing reliance on subjective experience in obtaining CAS scores. TAO (dpeaa)DE-He213 Wavelet transform (dpeaa)DE-He213 Multi-scale cascaded attention mechanism (dpeaa)DE-He213 Residual deformable convolution (dpeaa)DE-He213 Facial image segmentation (dpeaa)DE-He213 Zhou, Huifang aut He, Hong (orcid)0000-0002-2584-2891 aut Chen, Jiayu aut Song, Xuefei aut Li, Kunhao aut Zhou, Lei aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 26 vom: 20. Juni, Seite 19145-19167 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:26 day:20 month:06 pages:19145-19167 https://dx.doi.org/10.1007/s00521-023-08727-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 26 20 06 19145-19167 |
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10.1007/s00521-023-08727-2 doi (DE-627)SPR05273661X (SPR)s00521-023-08727-2-e DE-627 ger DE-627 rakwb eng Zhu, Haipeng verfasserin aut A novel encoder–decoder wavelet model for multifocal region segmentation of TAO facial images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Thyroid-associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on patients` life and health of. Clinically, the Clinical Activity Score (CAS) is one of the crucial methods for the early diagnosis of TAO. However, due to the diversity of TAO symptoms, utilizing medical expertise to artificially obtain CAS scores is challenging and highly dependent on personal subjectivity. Therefore, accurate identification of TAO regions segmented by scientific techniques is one of the essential prerequisites for the objective acquisition of the CAS scores. In this study, an encoder–decoder wavelet model (EDWM) with multiple-scale cascaded attention mechanism (MCAM) and residual deformable convolution (RDC) was proposed for multifocal region segmentation of TAO from facial images. The proposed method employs the discrete wavelet transform (DWT) to construct an encoder structure for the coarse feature extraction of the diseased regions. The inverse wavelet transform (IWT) is designed to build a decoder structure for resolution recovery. Meanwhile, the MCAM is developed to extract finer features of adjacent wavelet scales in the encoder structure by suppressing the background and focusing on the coarse segmentation of the diseased regions. The RDC is ultimately utilized for enlargement of arbitrary receptive fields and the accurate multi-segmentation task in different regions. In comparison with other selected benchmark models, the EDWM has, respectively, achieved state-of-the-art segmentation performance with 93.12% and 0.804 of the precision and the MIoU when tested on the images of 600 TAO patients. Since the EDWM is characterized by compact structure, interpretability, and strong feature extraction capability, it can provide a much more reliable and scientific basis for the early detection and diagnosis of TAO, reducing reliance on subjective experience in obtaining CAS scores. TAO (dpeaa)DE-He213 Wavelet transform (dpeaa)DE-He213 Multi-scale cascaded attention mechanism (dpeaa)DE-He213 Residual deformable convolution (dpeaa)DE-He213 Facial image segmentation (dpeaa)DE-He213 Zhou, Huifang aut He, Hong (orcid)0000-0002-2584-2891 aut Chen, Jiayu aut Song, Xuefei aut Li, Kunhao aut Zhou, Lei aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 26 vom: 20. Juni, Seite 19145-19167 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:26 day:20 month:06 pages:19145-19167 https://dx.doi.org/10.1007/s00521-023-08727-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 26 20 06 19145-19167 |
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10.1007/s00521-023-08727-2 doi (DE-627)SPR05273661X (SPR)s00521-023-08727-2-e DE-627 ger DE-627 rakwb eng Zhu, Haipeng verfasserin aut A novel encoder–decoder wavelet model for multifocal region segmentation of TAO facial images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Thyroid-associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on patients` life and health of. Clinically, the Clinical Activity Score (CAS) is one of the crucial methods for the early diagnosis of TAO. However, due to the diversity of TAO symptoms, utilizing medical expertise to artificially obtain CAS scores is challenging and highly dependent on personal subjectivity. Therefore, accurate identification of TAO regions segmented by scientific techniques is one of the essential prerequisites for the objective acquisition of the CAS scores. In this study, an encoder–decoder wavelet model (EDWM) with multiple-scale cascaded attention mechanism (MCAM) and residual deformable convolution (RDC) was proposed for multifocal region segmentation of TAO from facial images. The proposed method employs the discrete wavelet transform (DWT) to construct an encoder structure for the coarse feature extraction of the diseased regions. The inverse wavelet transform (IWT) is designed to build a decoder structure for resolution recovery. Meanwhile, the MCAM is developed to extract finer features of adjacent wavelet scales in the encoder structure by suppressing the background and focusing on the coarse segmentation of the diseased regions. The RDC is ultimately utilized for enlargement of arbitrary receptive fields and the accurate multi-segmentation task in different regions. In comparison with other selected benchmark models, the EDWM has, respectively, achieved state-of-the-art segmentation performance with 93.12% and 0.804 of the precision and the MIoU when tested on the images of 600 TAO patients. Since the EDWM is characterized by compact structure, interpretability, and strong feature extraction capability, it can provide a much more reliable and scientific basis for the early detection and diagnosis of TAO, reducing reliance on subjective experience in obtaining CAS scores. TAO (dpeaa)DE-He213 Wavelet transform (dpeaa)DE-He213 Multi-scale cascaded attention mechanism (dpeaa)DE-He213 Residual deformable convolution (dpeaa)DE-He213 Facial image segmentation (dpeaa)DE-He213 Zhou, Huifang aut He, Hong (orcid)0000-0002-2584-2891 aut Chen, Jiayu aut Song, Xuefei aut Li, Kunhao aut Zhou, Lei aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 26 vom: 20. Juni, Seite 19145-19167 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:26 day:20 month:06 pages:19145-19167 https://dx.doi.org/10.1007/s00521-023-08727-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 26 20 06 19145-19167 |
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10.1007/s00521-023-08727-2 doi (DE-627)SPR05273661X (SPR)s00521-023-08727-2-e DE-627 ger DE-627 rakwb eng Zhu, Haipeng verfasserin aut A novel encoder–decoder wavelet model for multifocal region segmentation of TAO facial images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Thyroid-associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on patients` life and health of. Clinically, the Clinical Activity Score (CAS) is one of the crucial methods for the early diagnosis of TAO. However, due to the diversity of TAO symptoms, utilizing medical expertise to artificially obtain CAS scores is challenging and highly dependent on personal subjectivity. Therefore, accurate identification of TAO regions segmented by scientific techniques is one of the essential prerequisites for the objective acquisition of the CAS scores. In this study, an encoder–decoder wavelet model (EDWM) with multiple-scale cascaded attention mechanism (MCAM) and residual deformable convolution (RDC) was proposed for multifocal region segmentation of TAO from facial images. The proposed method employs the discrete wavelet transform (DWT) to construct an encoder structure for the coarse feature extraction of the diseased regions. The inverse wavelet transform (IWT) is designed to build a decoder structure for resolution recovery. Meanwhile, the MCAM is developed to extract finer features of adjacent wavelet scales in the encoder structure by suppressing the background and focusing on the coarse segmentation of the diseased regions. The RDC is ultimately utilized for enlargement of arbitrary receptive fields and the accurate multi-segmentation task in different regions. In comparison with other selected benchmark models, the EDWM has, respectively, achieved state-of-the-art segmentation performance with 93.12% and 0.804 of the precision and the MIoU when tested on the images of 600 TAO patients. Since the EDWM is characterized by compact structure, interpretability, and strong feature extraction capability, it can provide a much more reliable and scientific basis for the early detection and diagnosis of TAO, reducing reliance on subjective experience in obtaining CAS scores. TAO (dpeaa)DE-He213 Wavelet transform (dpeaa)DE-He213 Multi-scale cascaded attention mechanism (dpeaa)DE-He213 Residual deformable convolution (dpeaa)DE-He213 Facial image segmentation (dpeaa)DE-He213 Zhou, Huifang aut He, Hong (orcid)0000-0002-2584-2891 aut Chen, Jiayu aut Song, Xuefei aut Li, Kunhao aut Zhou, Lei aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 26 vom: 20. Juni, Seite 19145-19167 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:26 day:20 month:06 pages:19145-19167 https://dx.doi.org/10.1007/s00521-023-08727-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 26 20 06 19145-19167 |
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Zhu, Haipeng @@aut@@ Zhou, Huifang @@aut@@ He, Hong @@aut@@ Chen, Jiayu @@aut@@ Song, Xuefei @@aut@@ Li, Kunhao @@aut@@ Zhou, Lei @@aut@@ |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Thyroid-associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on patients` life and health of. Clinically, the Clinical Activity Score (CAS) is one of the crucial methods for the early diagnosis of TAO. However, due to the diversity of TAO symptoms, utilizing medical expertise to artificially obtain CAS scores is challenging and highly dependent on personal subjectivity. Therefore, accurate identification of TAO regions segmented by scientific techniques is one of the essential prerequisites for the objective acquisition of the CAS scores. In this study, an encoder–decoder wavelet model (EDWM) with multiple-scale cascaded attention mechanism (MCAM) and residual deformable convolution (RDC) was proposed for multifocal region segmentation of TAO from facial images. The proposed method employs the discrete wavelet transform (DWT) to construct an encoder structure for the coarse feature extraction of the diseased regions. The inverse wavelet transform (IWT) is designed to build a decoder structure for resolution recovery. Meanwhile, the MCAM is developed to extract finer features of adjacent wavelet scales in the encoder structure by suppressing the background and focusing on the coarse segmentation of the diseased regions. The RDC is ultimately utilized for enlargement of arbitrary receptive fields and the accurate multi-segmentation task in different regions. In comparison with other selected benchmark models, the EDWM has, respectively, achieved state-of-the-art segmentation performance with 93.12% and 0.804 of the precision and the MIoU when tested on the images of 600 TAO patients. Since the EDWM is characterized by compact structure, interpretability, and strong feature extraction capability, it can provide a much more reliable and scientific basis for the early detection and diagnosis of TAO, reducing reliance on subjective experience in obtaining CAS scores.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">TAO</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wavelet transform</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multi-scale cascaded attention mechanism</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Residual deformable convolution</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Facial image segmentation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Huifang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">He, Hong</subfield><subfield code="0">(orcid)0000-0002-2584-2891</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Jiayu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Song, Xuefei</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Kunhao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Lei</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">London : Springer, 1993</subfield><subfield code="g">35(2023), 26 vom: 20. 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Zhu, Haipeng |
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Zhu, Haipeng misc TAO misc Wavelet transform misc Multi-scale cascaded attention mechanism misc Residual deformable convolution misc Facial image segmentation A novel encoder–decoder wavelet model for multifocal region segmentation of TAO facial images |
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novel encoder–decoder wavelet model for multifocal region segmentation of tao facial images |
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A novel encoder–decoder wavelet model for multifocal region segmentation of TAO facial images |
| abstract |
Abstract Thyroid-associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on patients` life and health of. Clinically, the Clinical Activity Score (CAS) is one of the crucial methods for the early diagnosis of TAO. However, due to the diversity of TAO symptoms, utilizing medical expertise to artificially obtain CAS scores is challenging and highly dependent on personal subjectivity. Therefore, accurate identification of TAO regions segmented by scientific techniques is one of the essential prerequisites for the objective acquisition of the CAS scores. In this study, an encoder–decoder wavelet model (EDWM) with multiple-scale cascaded attention mechanism (MCAM) and residual deformable convolution (RDC) was proposed for multifocal region segmentation of TAO from facial images. The proposed method employs the discrete wavelet transform (DWT) to construct an encoder structure for the coarse feature extraction of the diseased regions. The inverse wavelet transform (IWT) is designed to build a decoder structure for resolution recovery. Meanwhile, the MCAM is developed to extract finer features of adjacent wavelet scales in the encoder structure by suppressing the background and focusing on the coarse segmentation of the diseased regions. The RDC is ultimately utilized for enlargement of arbitrary receptive fields and the accurate multi-segmentation task in different regions. In comparison with other selected benchmark models, the EDWM has, respectively, achieved state-of-the-art segmentation performance with 93.12% and 0.804 of the precision and the MIoU when tested on the images of 600 TAO patients. Since the EDWM is characterized by compact structure, interpretability, and strong feature extraction capability, it can provide a much more reliable and scientific basis for the early detection and diagnosis of TAO, reducing reliance on subjective experience in obtaining CAS scores. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Thyroid-associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on patients` life and health of. Clinically, the Clinical Activity Score (CAS) is one of the crucial methods for the early diagnosis of TAO. However, due to the diversity of TAO symptoms, utilizing medical expertise to artificially obtain CAS scores is challenging and highly dependent on personal subjectivity. Therefore, accurate identification of TAO regions segmented by scientific techniques is one of the essential prerequisites for the objective acquisition of the CAS scores. In this study, an encoder–decoder wavelet model (EDWM) with multiple-scale cascaded attention mechanism (MCAM) and residual deformable convolution (RDC) was proposed for multifocal region segmentation of TAO from facial images. The proposed method employs the discrete wavelet transform (DWT) to construct an encoder structure for the coarse feature extraction of the diseased regions. The inverse wavelet transform (IWT) is designed to build a decoder structure for resolution recovery. Meanwhile, the MCAM is developed to extract finer features of adjacent wavelet scales in the encoder structure by suppressing the background and focusing on the coarse segmentation of the diseased regions. The RDC is ultimately utilized for enlargement of arbitrary receptive fields and the accurate multi-segmentation task in different regions. In comparison with other selected benchmark models, the EDWM has, respectively, achieved state-of-the-art segmentation performance with 93.12% and 0.804 of the precision and the MIoU when tested on the images of 600 TAO patients. Since the EDWM is characterized by compact structure, interpretability, and strong feature extraction capability, it can provide a much more reliable and scientific basis for the early detection and diagnosis of TAO, reducing reliance on subjective experience in obtaining CAS scores. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Thyroid-associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on patients` life and health of. Clinically, the Clinical Activity Score (CAS) is one of the crucial methods for the early diagnosis of TAO. However, due to the diversity of TAO symptoms, utilizing medical expertise to artificially obtain CAS scores is challenging and highly dependent on personal subjectivity. Therefore, accurate identification of TAO regions segmented by scientific techniques is one of the essential prerequisites for the objective acquisition of the CAS scores. In this study, an encoder–decoder wavelet model (EDWM) with multiple-scale cascaded attention mechanism (MCAM) and residual deformable convolution (RDC) was proposed for multifocal region segmentation of TAO from facial images. The proposed method employs the discrete wavelet transform (DWT) to construct an encoder structure for the coarse feature extraction of the diseased regions. The inverse wavelet transform (IWT) is designed to build a decoder structure for resolution recovery. Meanwhile, the MCAM is developed to extract finer features of adjacent wavelet scales in the encoder structure by suppressing the background and focusing on the coarse segmentation of the diseased regions. The RDC is ultimately utilized for enlargement of arbitrary receptive fields and the accurate multi-segmentation task in different regions. In comparison with other selected benchmark models, the EDWM has, respectively, achieved state-of-the-art segmentation performance with 93.12% and 0.804 of the precision and the MIoU when tested on the images of 600 TAO patients. Since the EDWM is characterized by compact structure, interpretability, and strong feature extraction capability, it can provide a much more reliable and scientific basis for the early detection and diagnosis of TAO, reducing reliance on subjective experience in obtaining CAS scores. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
A novel encoder–decoder wavelet model for multifocal region segmentation of TAO facial images |
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https://dx.doi.org/10.1007/s00521-023-08727-2 |
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Zhou, Huifang He, Hong Chen, Jiayu Song, Xuefei Li, Kunhao Zhou, Lei |
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Zhou, Huifang He, Hong Chen, Jiayu Song, Xuefei Li, Kunhao Zhou, Lei |
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| doi_str |
10.1007/s00521-023-08727-2 |
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2024-07-03T14:25:19.883Z |
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| score |
7.4013977 |