Alzheimer's disease classification device and method based on multi-task graph isomorphic network
The invention provides an Alzheimer's disease classification device and method based on a multi-task graph isomorphic network, and relates to the technical field of Alzheimer's disease classification. The method comprises the following steps: preprocessing M groups of functional magnetic r...
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| creator | ZHONG WEIYING LI SHUO XU HUANGE WANG ZHIQIONG LIN ZICAN |
| description | The invention provides an Alzheimer's disease classification device and method based on a multi-task graph isomorphic network, and relates to the technical field of Alzheimer's disease classification. The method comprises the following steps: preprocessing M groups of functional magnetic resonance imaging data of AD patients and normal persons through an fMRI data preprocessing module to obtain M groups of standard functional magnetic resonance imaging; performing time sequence extraction by using the obtained standard function magnetic resonance imaging through a data extraction module, constructing a brain function network, and extracting the characteristics of nodes in the brain function network from the obtained M groups of binary matrixes, in the classification prediction module, node feature fusion is carried out on data obtained from a previous module by using a graph isomorphic network, new data obtained after node fusion is used for training an Alzheimer's disease classification task, and then two st |
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The method comprises the following steps: preprocessing M groups of functional magnetic resonance imaging data of AD patients and normal persons through an fMRI data preprocessing module to obtain M groups of standard functional magnetic resonance imaging; performing time sequence extraction by using the obtained standard function magnetic resonance imaging through a data extraction module, constructing a brain function network, and extracting the characteristics of nodes in the brain function network from the obtained M groups of binary matrixes, in the classification prediction module, node feature fusion is carried out on data obtained from a previous module by using a graph isomorphic network, new data obtained after node fusion is used for training an Alzheimer's disease classification task, and then two st</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS ; PHYSICS</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230314&DB=EPODOC&CC=CN&NR=115798709A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230314&DB=EPODOC&CC=CN&NR=115798709A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHONG WEIYING</creatorcontrib><creatorcontrib>LI SHUO</creatorcontrib><creatorcontrib>XU HUANGE</creatorcontrib><creatorcontrib>WANG ZHIQIONG</creatorcontrib><creatorcontrib>LIN ZICAN</creatorcontrib><title>Alzheimer's disease classification device and method based on multi-task graph isomorphic network</title><description>The invention provides an Alzheimer's disease classification device and method based on a multi-task graph isomorphic network, and relates to the technical field of Alzheimer's disease classification. The method comprises the following steps: preprocessing M groups of functional magnetic resonance imaging data of AD patients and normal persons through an fMRI data preprocessing module to obtain M groups of standard functional magnetic resonance imaging; performing time sequence extraction by using the obtained standard function magnetic resonance imaging through a data extraction module, constructing a brain function network, and extracting the characteristics of nodes in the brain function network from the obtained M groups of binary matrixes, in the classification prediction module, node feature fusion is carried out on data obtained from a previous module by using a graph isomorphic network, new data obtained after node fusion is used for training an Alzheimer's disease classification task, and then two st</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyj0KwkAQQOE0FqLeYaysAgaRmDIExcrKPoy7k-yQ_WNnVfD0pvAAVq9437LA1n4MsaO0E9AshEKgLIrwwAozBw-aXqwI0GtwlE3Q8JiVhnm5p81cZpQJxoTRAEtwIUXDCjzld0jTulgMaIU2v66K7eV8764lxdCTRFQ0y767VdWxbk71vmkP_5gvJWw-Dw</recordid><startdate>20230314</startdate><enddate>20230314</enddate><creator>ZHONG WEIYING</creator><creator>LI SHUO</creator><creator>XU HUANGE</creator><creator>WANG ZHIQIONG</creator><creator>LIN ZICAN</creator><scope>EVB</scope></search><sort><creationdate>20230314</creationdate><title>Alzheimer's disease classification device and method based on multi-task graph isomorphic network</title><author>ZHONG WEIYING ; LI SHUO ; XU HUANGE ; WANG ZHIQIONG ; LIN ZICAN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN115798709A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>ZHONG WEIYING</creatorcontrib><creatorcontrib>LI SHUO</creatorcontrib><creatorcontrib>XU HUANGE</creatorcontrib><creatorcontrib>WANG ZHIQIONG</creatorcontrib><creatorcontrib>LIN ZICAN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZHONG WEIYING</au><au>LI SHUO</au><au>XU HUANGE</au><au>WANG ZHIQIONG</au><au>LIN ZICAN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Alzheimer's disease classification device and method based on multi-task graph isomorphic network</title><date>2023-03-14</date><risdate>2023</risdate><abstract>The invention provides an Alzheimer's disease classification device and method based on a multi-task graph isomorphic network, and relates to the technical field of Alzheimer's disease classification. The method comprises the following steps: preprocessing M groups of functional magnetic resonance imaging data of AD patients and normal persons through an fMRI data preprocessing module to obtain M groups of standard functional magnetic resonance imaging; performing time sequence extraction by using the obtained standard function magnetic resonance imaging through a data extraction module, constructing a brain function network, and extracting the characteristics of nodes in the brain function network from the obtained M groups of binary matrixes, in the classification prediction module, node feature fusion is carried out on data obtained from a previous module by using a graph isomorphic network, new data obtained after node fusion is used for training an Alzheimer's disease classification task, and then two st</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTING COUNTING HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA IMAGE DATA PROCESSING OR GENERATION, IN GENERAL INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS PHYSICS |
| title | Alzheimer's disease classification device and method based on multi-task graph isomorphic network |
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