{"id":"https://openalex.org/W4405520248","doi":"https://doi.org/10.1109/tai.2024.3517570","title":"Energy-Efficient Hybrid Impulsive Model for Joint Classification and Segmentation on CT Images","display_name":"Energy-Efficient Hybrid Impulsive Model for Joint Classification and Segmentation on CT Images","publication_year":2024,"publication_date":"2024-12-18","ids":{"openalex":"https://openalex.org/W4405520248","doi":"https://doi.org/10.1109/tai.2024.3517570"},"language":"en","primary_location":{"id":"doi:10.1109/tai.2024.3517570","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2024.3517570","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016980595","display_name":"Bin Hu","orcid":"https://orcid.org/0000-0002-8851-4561"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Hu","raw_affiliation_strings":["School of Future Technology, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-8851-4561","affiliations":[{"raw_affiliation_string":"School of Future Technology, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043199495","display_name":"Zhi\u2010Hong Guan","orcid":"https://orcid.org/0000-0001-7997-0314"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi-Hong Guan","raw_affiliation_strings":["School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-7997-0314","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024633466","display_name":"Guanrong Chen","orcid":"https://orcid.org/0000-0003-1381-7418"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Guanrong Chen","raw_affiliation_strings":["Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0003-1381-7418","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085207296","display_name":"J\u00fcrgen Kurths","orcid":"https://orcid.org/0000-0002-5926-4276"},"institutions":[{"id":"https://openalex.org/I39343248","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992","country_code":"DE","type":"education","lineage":["https://openalex.org/I39343248"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"J\u00fcrgen Kurths","raw_affiliation_strings":["Institute of Physics, Humboldt-Universität zu Berlin, Berlin, Germany"],"raw_orcid":"https://orcid.org/0000-0002-5926-4276","affiliations":[{"raw_affiliation_string":"Institute of Physics, Humboldt-Universität zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5991,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.69907859,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"6","issue":"5","first_page":"1401","last_page":"1413"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9758999943733215,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9733999967575073,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.7434912919998169},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7195476293563843},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6323451399803162},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5262200832366943},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.523349940776825},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.47500550746917725},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4701678156852722},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15563735365867615},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14304021000862122},{"id":"https://openalex.org/keywords/structural-engineering","display_name":"Structural engineering","score":0.11097538471221924},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0590549111366272}],"concepts":[{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.7434912919998169},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7195476293563843},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6323451399803162},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5262200832366943},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.523349940776825},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.47500550746917725},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4701678156852722},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15563735365867615},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14304021000862122},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.11097538471221924},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0590549111366272}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tai.2024.3517570","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2024.3517570","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.7900000214576721,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G2663303846","display_name":null,"funder_award_id":"62322311","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5834366670","display_name":null,"funder_award_id":"62233007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1570411240","https://openalex.org/W1901129140","https://openalex.org/W2153039279","https://openalex.org/W2621826044","https://openalex.org/W2783525259","https://openalex.org/W2896879375","https://openalex.org/W2921406441","https://openalex.org/W2963351448","https://openalex.org/W2966081953","https://openalex.org/W2990793844","https://openalex.org/W3006300626","https://openalex.org/W3007283957","https://openalex.org/W3016950572","https://openalex.org/W3027763298","https://openalex.org/W3028070348","https://openalex.org/W3045168954","https://openalex.org/W3094502228","https://openalex.org/W3104739447","https://openalex.org/W3124546450","https://openalex.org/W3127751679","https://openalex.org/W3135613337","https://openalex.org/W3155495622","https://openalex.org/W3157474654","https://openalex.org/W3201244874","https://openalex.org/W3206513151","https://openalex.org/W4206452160","https://openalex.org/W4225275189","https://openalex.org/W4281399757","https://openalex.org/W4285133390","https://openalex.org/W4293146242","https://openalex.org/W4312383539","https://openalex.org/W4312568229","https://openalex.org/W4317721859","https://openalex.org/W4318823721","https://openalex.org/W4320802369","https://openalex.org/W4365143687","https://openalex.org/W4366352025","https://openalex.org/W6637373629","https://openalex.org/W6763002979","https://openalex.org/W6790275670","https://openalex.org/W6852107617"],"related_works":["https://openalex.org/W1996130883","https://openalex.org/W2748574964","https://openalex.org/W2888483922","https://openalex.org/W4396737233","https://openalex.org/W4379231730","https://openalex.org/W2367747139","https://openalex.org/W4389858081","https://openalex.org/W4391102217","https://openalex.org/W2566187525","https://openalex.org/W2566334511"],"abstract_inverted_index":{"Highly":[0],"flexible":[1,80],"foundation":[2],"models":[3,145],"like":[4],"artificial":[5],"neural":[6],"networks":[7],"are":[8,71],"imperative":[9],"in":[10,42],"medical":[11],"practice,":[12],"enabling":[13],"diverse":[14],"tasks":[15,47],"with":[16,83],"little":[17],"or":[18],"no":[19],"task-specific":[20,111],"labeled":[21],"data.":[22],"The":[23,98],"crucial":[24],"problem":[25],"remains":[26],"as":[27],"how":[28],"to":[29,73,117],"link":[30],"latent":[31],"features":[32,93],"and":[33,45,64,96,122,130,136,146,157,172],"a":[34,53,79,167],"priori":[35],"knowledge":[36],"within":[37],"multitask":[38],"decision":[39],"outputs,":[40],"particularly":[41],"joint":[43,99],"classification":[44,95,105],"segmentation":[46],"on":[48],"images.":[49,115],"This":[50],"article":[51],"develops":[52],"hybrid":[54,58,88,140,163],"encoder-decoding":[55],"model":[56,89],"substantiating":[57],"computations":[59],"of":[60,94,138,161],"continuous":[61],"convolution":[62,144],"variables":[63],"discrete":[65],"nerve":[66],"impulses,":[67],"where":[68],"impulsive":[69,164],"neurons":[70],"adopted":[72],"boost":[74],"nonlinear":[75],"activations.":[76],"By":[77],"presenting":[78],"network":[81],"architecture":[82],"regularized":[84],"multiloss":[85],"training,":[86],"this":[87,139],"can":[90],"learn":[91],"shared":[92],"segmentation.":[97],"decoder":[100],"does":[101],"not":[102],"only":[103],"provide":[104],"results,":[106],"but":[107],"also":[108],"predicts":[109],"intelligible":[110],"outputs":[112],"from":[113],"input":[114],"Applied":[116],"the":[118,123,134,153,158,162],"COVID-19":[119],"lung":[120],"CT":[121,126],"Synapse":[124],"multiorgan":[125],"datasets,":[127],"experimental":[128],"results":[129],"ablation":[131],"studies":[132,150],"demonstrate":[133],"effectiveness":[135],"flexibility":[137],"model,":[141,165],"which":[142],"outperforms":[143],"human":[147],"experts.":[148],"Comparative":[149],"further":[151],"highlight":[152],"high":[154],"energy-efficient":[155],"attribute":[156],"decision-output":[159],"visibility":[160],"indicating":[166],"potential":[168],"for":[169],"edge":[170],"healthcare":[171],"biomedical":[173],"applications.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}