Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion

Abstract Alzheimer’s disease (AD) is an advanced and incurable neurodegenerative disease. Genetic variations are intrinsic etiological factors contributing to the abnormal expression of brain function and structure in AD patients. A new multimodal feature fusion called “magnetic resonance imaging (M...
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Autor*in:	Wang, Jingru [verfasserIn] Wen, Shipeng [verfasserIn] Liu, Wenjie [verfasserIn] Meng, Xianglian [verfasserIn] Jiao, Zhuqing [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Alzheimer’s disease Multimodal feature fusion Deep joint learning diagnosis Attention mechanism ResNet

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: BioData Mining - BioMed Central, 2008, 17(2024), 1 vom: 05. Nov.
Übergeordnetes Werk:	volume:17 ; year:2024 ; number:1 ; day:05 ; month:11

Links:	Volltext

DOI / URN:	10.1186/s13040-024-00395-9

Katalog-ID:	SPR058282769

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allfields	10.1186/s13040-024-00395-9 doi (DE-627)SPR058282769 (SPR)s13040-024-00395-9-e DE-627 ger DE-627 rakwb eng 570 540 VZ BIODIV DE-30 fid Wang, Jingru verfasserin aut Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Alzheimer’s disease (AD) is an advanced and incurable neurodegenerative disease. Genetic variations are intrinsic etiological factors contributing to the abnormal expression of brain function and structure in AD patients. A new multimodal feature fusion called “magnetic resonance imaging (MRI)-p value” was proposed to construct 3D fusion images by introducing genes as a priori knowledge. Moreover, a new deep joint learning diagnostic model was constructed to fully learn images features. One branch trained a residual network (ResNet) to learn the features of local pathological regions. The other branch learned the position information of brain regions with different changes in the different categories of subjects’ brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. The feature and position information of the two branches were linearly interacted to acquire the diagnostic basis for classifying the different categories of subjects. The diagnoses of AD and health control (HC), AD and mild cognitive impairment (MCI), HC and MCI were performed with data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results showed that the proposed method achieved optimal results in AD-related diagnosis. The classification accuracy (ACC) and area under the curve (AUC) of the three experimental groups were 93.44% and 96.67%, 89.06% and 92%, and 84% and 81.84%, respectively. Moreover, a total of six novel genes were found to be significantly associated with AD, namely NTM, MAML2, NAALADL2, FHIT, TMEM132D and PCSK5, which provided new targets for the potential treatment of neurodegenerative diseases. Alzheimer’s disease (dpeaa)DE-He213 Multimodal feature fusion (dpeaa)DE-He213 Deep joint learning diagnosis (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 ResNet (dpeaa)DE-He213 Wen, Shipeng verfasserin aut Liu, Wenjie verfasserin aut Meng, Xianglian verfasserin aut Jiao, Zhuqing verfasserin aut Enthalten in BioData Mining BioMed Central, 2008 17(2024), 1 vom: 05. Nov. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:17 year:2024 number:1 day:05 month:11 https://dx.doi.org/10.1186/s13040-024-00395-9 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 05 11
spelling	10.1186/s13040-024-00395-9 doi (DE-627)SPR058282769 (SPR)s13040-024-00395-9-e DE-627 ger DE-627 rakwb eng 570 540 VZ BIODIV DE-30 fid Wang, Jingru verfasserin aut Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Alzheimer’s disease (AD) is an advanced and incurable neurodegenerative disease. Genetic variations are intrinsic etiological factors contributing to the abnormal expression of brain function and structure in AD patients. A new multimodal feature fusion called “magnetic resonance imaging (MRI)-p value” was proposed to construct 3D fusion images by introducing genes as a priori knowledge. Moreover, a new deep joint learning diagnostic model was constructed to fully learn images features. One branch trained a residual network (ResNet) to learn the features of local pathological regions. The other branch learned the position information of brain regions with different changes in the different categories of subjects’ brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. The feature and position information of the two branches were linearly interacted to acquire the diagnostic basis for classifying the different categories of subjects. The diagnoses of AD and health control (HC), AD and mild cognitive impairment (MCI), HC and MCI were performed with data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results showed that the proposed method achieved optimal results in AD-related diagnosis. The classification accuracy (ACC) and area under the curve (AUC) of the three experimental groups were 93.44% and 96.67%, 89.06% and 92%, and 84% and 81.84%, respectively. Moreover, a total of six novel genes were found to be significantly associated with AD, namely NTM, MAML2, NAALADL2, FHIT, TMEM132D and PCSK5, which provided new targets for the potential treatment of neurodegenerative diseases. Alzheimer’s disease (dpeaa)DE-He213 Multimodal feature fusion (dpeaa)DE-He213 Deep joint learning diagnosis (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 ResNet (dpeaa)DE-He213 Wen, Shipeng verfasserin aut Liu, Wenjie verfasserin aut Meng, Xianglian verfasserin aut Jiao, Zhuqing verfasserin aut Enthalten in BioData Mining BioMed Central, 2008 17(2024), 1 vom: 05. Nov. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:17 year:2024 number:1 day:05 month:11 https://dx.doi.org/10.1186/s13040-024-00395-9 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 05 11
allfields_unstemmed	10.1186/s13040-024-00395-9 doi (DE-627)SPR058282769 (SPR)s13040-024-00395-9-e DE-627 ger DE-627 rakwb eng 570 540 VZ BIODIV DE-30 fid Wang, Jingru verfasserin aut Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Alzheimer’s disease (AD) is an advanced and incurable neurodegenerative disease. Genetic variations are intrinsic etiological factors contributing to the abnormal expression of brain function and structure in AD patients. A new multimodal feature fusion called “magnetic resonance imaging (MRI)-p value” was proposed to construct 3D fusion images by introducing genes as a priori knowledge. Moreover, a new deep joint learning diagnostic model was constructed to fully learn images features. One branch trained a residual network (ResNet) to learn the features of local pathological regions. The other branch learned the position information of brain regions with different changes in the different categories of subjects’ brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. The feature and position information of the two branches were linearly interacted to acquire the diagnostic basis for classifying the different categories of subjects. The diagnoses of AD and health control (HC), AD and mild cognitive impairment (MCI), HC and MCI were performed with data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results showed that the proposed method achieved optimal results in AD-related diagnosis. The classification accuracy (ACC) and area under the curve (AUC) of the three experimental groups were 93.44% and 96.67%, 89.06% and 92%, and 84% and 81.84%, respectively. Moreover, a total of six novel genes were found to be significantly associated with AD, namely NTM, MAML2, NAALADL2, FHIT, TMEM132D and PCSK5, which provided new targets for the potential treatment of neurodegenerative diseases. Alzheimer’s disease (dpeaa)DE-He213 Multimodal feature fusion (dpeaa)DE-He213 Deep joint learning diagnosis (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 ResNet (dpeaa)DE-He213 Wen, Shipeng verfasserin aut Liu, Wenjie verfasserin aut Meng, Xianglian verfasserin aut Jiao, Zhuqing verfasserin aut Enthalten in BioData Mining BioMed Central, 2008 17(2024), 1 vom: 05. Nov. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:17 year:2024 number:1 day:05 month:11 https://dx.doi.org/10.1186/s13040-024-00395-9 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 05 11
allfieldsGer	10.1186/s13040-024-00395-9 doi (DE-627)SPR058282769 (SPR)s13040-024-00395-9-e DE-627 ger DE-627 rakwb eng 570 540 VZ BIODIV DE-30 fid Wang, Jingru verfasserin aut Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Alzheimer’s disease (AD) is an advanced and incurable neurodegenerative disease. Genetic variations are intrinsic etiological factors contributing to the abnormal expression of brain function and structure in AD patients. A new multimodal feature fusion called “magnetic resonance imaging (MRI)-p value” was proposed to construct 3D fusion images by introducing genes as a priori knowledge. Moreover, a new deep joint learning diagnostic model was constructed to fully learn images features. One branch trained a residual network (ResNet) to learn the features of local pathological regions. The other branch learned the position information of brain regions with different changes in the different categories of subjects’ brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. The feature and position information of the two branches were linearly interacted to acquire the diagnostic basis for classifying the different categories of subjects. The diagnoses of AD and health control (HC), AD and mild cognitive impairment (MCI), HC and MCI were performed with data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results showed that the proposed method achieved optimal results in AD-related diagnosis. The classification accuracy (ACC) and area under the curve (AUC) of the three experimental groups were 93.44% and 96.67%, 89.06% and 92%, and 84% and 81.84%, respectively. Moreover, a total of six novel genes were found to be significantly associated with AD, namely NTM, MAML2, NAALADL2, FHIT, TMEM132D and PCSK5, which provided new targets for the potential treatment of neurodegenerative diseases. Alzheimer’s disease (dpeaa)DE-He213 Multimodal feature fusion (dpeaa)DE-He213 Deep joint learning diagnosis (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 ResNet (dpeaa)DE-He213 Wen, Shipeng verfasserin aut Liu, Wenjie verfasserin aut Meng, Xianglian verfasserin aut Jiao, Zhuqing verfasserin aut Enthalten in BioData Mining BioMed Central, 2008 17(2024), 1 vom: 05. Nov. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:17 year:2024 number:1 day:05 month:11 https://dx.doi.org/10.1186/s13040-024-00395-9 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 05 11
allfieldsSound	10.1186/s13040-024-00395-9 doi (DE-627)SPR058282769 (SPR)s13040-024-00395-9-e DE-627 ger DE-627 rakwb eng 570 540 VZ BIODIV DE-30 fid Wang, Jingru verfasserin aut Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Alzheimer’s disease (AD) is an advanced and incurable neurodegenerative disease. Genetic variations are intrinsic etiological factors contributing to the abnormal expression of brain function and structure in AD patients. A new multimodal feature fusion called “magnetic resonance imaging (MRI)-p value” was proposed to construct 3D fusion images by introducing genes as a priori knowledge. Moreover, a new deep joint learning diagnostic model was constructed to fully learn images features. One branch trained a residual network (ResNet) to learn the features of local pathological regions. The other branch learned the position information of brain regions with different changes in the different categories of subjects’ brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. The feature and position information of the two branches were linearly interacted to acquire the diagnostic basis for classifying the different categories of subjects. The diagnoses of AD and health control (HC), AD and mild cognitive impairment (MCI), HC and MCI were performed with data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results showed that the proposed method achieved optimal results in AD-related diagnosis. The classification accuracy (ACC) and area under the curve (AUC) of the three experimental groups were 93.44% and 96.67%, 89.06% and 92%, and 84% and 81.84%, respectively. Moreover, a total of six novel genes were found to be significantly associated with AD, namely NTM, MAML2, NAALADL2, FHIT, TMEM132D and PCSK5, which provided new targets for the potential treatment of neurodegenerative diseases. Alzheimer’s disease (dpeaa)DE-He213 Multimodal feature fusion (dpeaa)DE-He213 Deep joint learning diagnosis (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 ResNet (dpeaa)DE-He213 Wen, Shipeng verfasserin aut Liu, Wenjie verfasserin aut Meng, Xianglian verfasserin aut Jiao, Zhuqing verfasserin aut Enthalten in BioData Mining BioMed Central, 2008 17(2024), 1 vom: 05. Nov. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:17 year:2024 number:1 day:05 month:11 https://dx.doi.org/10.1186/s13040-024-00395-9 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER FID-BIODIV SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 05 11
language	English
source	Enthalten in BioData Mining 17(2024), 1 vom: 05. Nov. volume:17 year:2024 number:1 day:05 month:11
sourceStr	Enthalten in BioData Mining 17(2024), 1 vom: 05. Nov. volume:17 year:2024 number:1 day:05 month:11
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Alzheimer’s disease Multimodal feature fusion Deep joint learning diagnosis Attention mechanism ResNet
dewey-raw	570
isfreeaccess_bool	true
container_title	BioData Mining
authorswithroles_txt_mv	Wang, Jingru @@aut@@ Wen, Shipeng @@aut@@ Liu, Wenjie @@aut@@ Meng, Xianglian @@aut@@ Jiao, Zhuqing @@aut@@
publishDateDaySort_date	2024-11-05T00:00:00Z
hierarchy_top_id	572421893
dewey-sort	3570
id	SPR058282769
language_de	englisch
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author	Wang, Jingru
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topic_title	570 540 VZ BIODIV DE-30 fid Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion Alzheimer’s disease (dpeaa)DE-He213 Multimodal feature fusion (dpeaa)DE-He213 Deep joint learning diagnosis (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 ResNet (dpeaa)DE-He213
topic	ddc 570 fid BIODIV misc Alzheimer’s disease misc Multimodal feature fusion misc Deep joint learning diagnosis misc Attention mechanism misc ResNet
topic_unstemmed	ddc 570 fid BIODIV misc Alzheimer’s disease misc Multimodal feature fusion misc Deep joint learning diagnosis misc Attention mechanism misc ResNet
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title	Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion
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title_full	Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion
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author_browse	Wang, Jingru Wen, Shipeng Liu, Wenjie Meng, Xianglian Jiao, Zhuqing
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title_sort	deep joint learning diagnosis of alzheimer’s disease based on multimodal feature fusion
title_auth	Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion
abstract	Abstract Alzheimer’s disease (AD) is an advanced and incurable neurodegenerative disease. Genetic variations are intrinsic etiological factors contributing to the abnormal expression of brain function and structure in AD patients. A new multimodal feature fusion called “magnetic resonance imaging (MRI)-p value” was proposed to construct 3D fusion images by introducing genes as a priori knowledge. Moreover, a new deep joint learning diagnostic model was constructed to fully learn images features. One branch trained a residual network (ResNet) to learn the features of local pathological regions. The other branch learned the position information of brain regions with different changes in the different categories of subjects’ brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. The feature and position information of the two branches were linearly interacted to acquire the diagnostic basis for classifying the different categories of subjects. The diagnoses of AD and health control (HC), AD and mild cognitive impairment (MCI), HC and MCI were performed with data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results showed that the proposed method achieved optimal results in AD-related diagnosis. The classification accuracy (ACC) and area under the curve (AUC) of the three experimental groups were 93.44% and 96.67%, 89.06% and 92%, and 84% and 81.84%, respectively. Moreover, a total of six novel genes were found to be significantly associated with AD, namely NTM, MAML2, NAALADL2, FHIT, TMEM132D and PCSK5, which provided new targets for the potential treatment of neurodegenerative diseases. © The Author(s) 2024
abstractGer	Abstract Alzheimer’s disease (AD) is an advanced and incurable neurodegenerative disease. Genetic variations are intrinsic etiological factors contributing to the abnormal expression of brain function and structure in AD patients. A new multimodal feature fusion called “magnetic resonance imaging (MRI)-p value” was proposed to construct 3D fusion images by introducing genes as a priori knowledge. Moreover, a new deep joint learning diagnostic model was constructed to fully learn images features. One branch trained a residual network (ResNet) to learn the features of local pathological regions. The other branch learned the position information of brain regions with different changes in the different categories of subjects’ brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. The feature and position information of the two branches were linearly interacted to acquire the diagnostic basis for classifying the different categories of subjects. The diagnoses of AD and health control (HC), AD and mild cognitive impairment (MCI), HC and MCI were performed with data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results showed that the proposed method achieved optimal results in AD-related diagnosis. The classification accuracy (ACC) and area under the curve (AUC) of the three experimental groups were 93.44% and 96.67%, 89.06% and 92%, and 84% and 81.84%, respectively. Moreover, a total of six novel genes were found to be significantly associated with AD, namely NTM, MAML2, NAALADL2, FHIT, TMEM132D and PCSK5, which provided new targets for the potential treatment of neurodegenerative diseases. © The Author(s) 2024
abstract_unstemmed	Abstract Alzheimer’s disease (AD) is an advanced and incurable neurodegenerative disease. Genetic variations are intrinsic etiological factors contributing to the abnormal expression of brain function and structure in AD patients. A new multimodal feature fusion called “magnetic resonance imaging (MRI)-p value” was proposed to construct 3D fusion images by introducing genes as a priori knowledge. Moreover, a new deep joint learning diagnostic model was constructed to fully learn images features. One branch trained a residual network (ResNet) to learn the features of local pathological regions. The other branch learned the position information of brain regions with different changes in the different categories of subjects’ brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. The feature and position information of the two branches were linearly interacted to acquire the diagnostic basis for classifying the different categories of subjects. The diagnoses of AD and health control (HC), AD and mild cognitive impairment (MCI), HC and MCI were performed with data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results showed that the proposed method achieved optimal results in AD-related diagnosis. The classification accuracy (ACC) and area under the curve (AUC) of the three experimental groups were 93.44% and 96.67%, 89.06% and 92%, and 84% and 81.84%, respectively. Moreover, a total of six novel genes were found to be significantly associated with AD, namely NTM, MAML2, NAALADL2, FHIT, TMEM132D and PCSK5, which provided new targets for the potential treatment of neurodegenerative diseases. © The Author(s) 2024
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title_short	Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion
url	https://dx.doi.org/10.1186/s13040-024-00395-9
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author2	Wen, Shipeng Liu, Wenjie Meng, Xianglian Jiao, Zhuqing
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