{"id":"https://openalex.org/W4318021318","doi":"https://doi.org/10.1109/mci.2022.3223487","title":"Self-Supervised Fusion for Multi-Modal Medical Images via Contrastive Auto-Encoding and Convolutional Information Exchange","display_name":"Self-Supervised Fusion for Multi-Modal Medical Images via Contrastive Auto-Encoding and Convolutional Information Exchange","publication_year":2023,"publication_date":"2023-01-25","ids":{"openalex":"https://openalex.org/W4318021318","doi":"https://doi.org/10.1109/mci.2022.3223487"},"language":"en","primary_location":{"id":"doi:10.1109/mci.2022.3223487","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mci.2022.3223487","pdf_url":null,"source":{"id":"https://openalex.org/S104797584","display_name":"IEEE Computational Intelligence Magazine","issn_l":"1556-603X","issn":["1556-603X","1556-6048"],"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 Computational Intelligence Magazine","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/A5100386125","display_name":"Ying Zhang","orcid":"https://orcid.org/0000-0002-3995-531X"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Zhang","raw_affiliation_strings":["Yunnan University, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University, CHINA","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080341760","display_name":"Rencan Nie","orcid":"https://orcid.org/0000-0003-0568-1231"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rencan Nie","raw_affiliation_strings":["Yunnan University, CHINA"],"raw_orcid":"https://orcid.org/0000-0003-0568-1231","affiliations":[{"raw_affiliation_string":"Yunnan University, CHINA","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017808266","display_name":"Jinde Cao","orcid":"https://orcid.org/0000-0003-3133-7119"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinde Cao","raw_affiliation_strings":["Southeast University, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University, CHINA","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069969960","display_name":"Chaozhen Ma","orcid":"https://orcid.org/0000-0001-7116-270X"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaozhen Ma","raw_affiliation_strings":["Yunnan University, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University, CHINA","institution_ids":["https://openalex.org/I189210763"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.4219,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.93199339,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"18","issue":"1","first_page":"68","last_page":"80"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/computer-science","display_name":"Computer science","score":0.807083010673523},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6637598276138306},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6538671851158142},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5957902669906616},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5750733017921448},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.49280479550361633},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.49116799235343933},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.4751436710357666},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4309638738632202},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.42796605825424194},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.41899847984313965},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.335263729095459},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1997832953929901},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09362542629241943}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.807083010673523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6637598276138306},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6538671851158142},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5957902669906616},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5750733017921448},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.49280479550361633},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.49116799235343933},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.4751436710357666},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4309638738632202},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.42796605825424194},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.41899847984313965},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.335263729095459},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1997832953929901},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09362542629241943},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mci.2022.3223487","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mci.2022.3223487","pdf_url":null,"source":{"id":"https://openalex.org/S104797584","display_name":"IEEE Computational Intelligence Magazine","issn_l":"1556-603X","issn":["1556-603X","1556-6048"],"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 Computational Intelligence Magazine","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2633796091","display_name":null,"funder_award_id":"2017M621586","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G3863222636","display_name":"\u9762\u5411\u591a\u6e90\u56fe\u50cf\u878d\u5408\u8d21\u732e\u4f30\u8ba1\u7684\u591a\u6e90\u8109\u51b2\u4fe1\u606f\u4ea4\u6362\u7f16\u7801\u4e0e\u5206\u6570\u9636\u53d8\u5206\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61966037","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4585534567","display_name":"\u57fa\u4e8e\u7fa4\u4f53\u667a\u80fd\u7684\u5206\u5e03\u5f0f\u4f18\u5316\u7406\u8bba\u3001\u65b9\u6cd5\u53ca\u5e94\u7528\u7814\u7a76","funder_award_id":"61833005","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"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1964189664","https://openalex.org/W1997480692","https://openalex.org/W2024926318","https://openalex.org/W2026651590","https://openalex.org/W2034995331","https://openalex.org/W2035226392","https://openalex.org/W2066667210","https://openalex.org/W2091484864","https://openalex.org/W2094947587","https://openalex.org/W2097061348","https://openalex.org/W2115167851","https://openalex.org/W2116702374","https://openalex.org/W2133046037","https://openalex.org/W2141983208","https://openalex.org/W2152583543","https://openalex.org/W2168783614","https://openalex.org/W2289413700","https://openalex.org/W2306859282","https://openalex.org/W2411377185","https://openalex.org/W2414218885","https://openalex.org/W2588907814","https://openalex.org/W2744198280","https://openalex.org/W2751011928","https://openalex.org/W2798405286","https://openalex.org/W2798987894","https://openalex.org/W2808591023","https://openalex.org/W2842511635","https://openalex.org/W2891631795","https://openalex.org/W2892817757","https://openalex.org/W2912551047","https://openalex.org/W2912581987","https://openalex.org/W2940940727","https://openalex.org/W2963787388","https://openalex.org/W2966009696","https://openalex.org/W2998012573","https://openalex.org/W2998529071","https://openalex.org/W3007473102","https://openalex.org/W3007891240","https://openalex.org/W3011667789","https://openalex.org/W3011768656","https://openalex.org/W3015396480","https://openalex.org/W3031508018","https://openalex.org/W3033116255","https://openalex.org/W3040984225","https://openalex.org/W3046194589","https://openalex.org/W3105639468","https://openalex.org/W3125032682","https://openalex.org/W3126442124","https://openalex.org/W3135530979","https://openalex.org/W3152753009","https://openalex.org/W3171420223","https://openalex.org/W4226422421","https://openalex.org/W4283080861","https://openalex.org/W4283732315","https://openalex.org/W4285391973","https://openalex.org/W4286587621","https://openalex.org/W4288696163","https://openalex.org/W4297808394","https://openalex.org/W4301621763","https://openalex.org/W6640793704","https://openalex.org/W6736057607"],"related_works":["https://openalex.org/W2669956259","https://openalex.org/W4321369474","https://openalex.org/W2731899572","https://openalex.org/W3133861977","https://openalex.org/W4200173597","https://openalex.org/W3116150086","https://openalex.org/W4287995534","https://openalex.org/W2998168123","https://openalex.org/W4312417841","https://openalex.org/W2606416966"],"abstract_inverted_index":{"This":[0,92],"paper":[1],"proposes":[2,61,94],"a":[3,8,62],"self-supervised":[4],"framework":[5],"based":[6,102],"on":[7,103],"contrastive":[9,50,64],"auto-encoding":[10],"and":[11,31,34,56,60,77,99,109,151],"convolutional":[12],"information":[13,35,89,135],"exchange":[14,136,141],"for":[15],"multi-modal":[16,25,126],"medical":[17,26,127],"fusion":[18,162,181],"tasks.":[19],"It":[20],"is":[21,37,122,138],"well":[22],"known":[23],"that":[24,171],"images":[27,72,85,147],"have":[28],"the":[29,83,88,97,130,142,153,160,165,175],"same":[30],"unique":[32],"features,":[33],"redundancy":[36,90],"easily":[38],"led":[39],"when":[40],"source":[41,71,84,146,167],"image":[42],"features":[43,101,155],"are":[44,73,156],"extracted":[45],"in":[46,66,113,148],"pairs.":[47],"Inspired":[48],"by":[49,106],"learning,":[51],"this":[52],"article":[53,93],"constructs":[54],"positive":[55,76],"negative":[57,78],"results":[58,79,177],"pairs":[59],"novel":[63],"loss":[65],"an":[67,116,134],"auto-encoder.":[68,117],"The":[69],"paired":[70,166],"considered":[74],"as":[75,115],"used":[80],"to":[81,86,124,140,158],"reconstruct":[82],"avoid":[87],"problem.":[91],"preserving":[95],"both":[96],"global":[98],"local":[100],"prior":[104],"knowledge":[105],"combining":[107],"transformer":[108],"convolution":[110],"neural":[111],"networks":[112],"parallel":[114],"A":[118],"contribution":[119,131,163],"estimation":[120,132],"model":[121],"adopted":[123],"fuse":[125],"images.":[128,168],"In":[129],"stage,":[133],"block":[137],"designed":[139],"feature":[143],"maps":[144],"of":[145,164],"multi-kernel":[149],"convolutions,":[150],"then":[152],"multi-convolutional":[154],"utilized":[157],"estimate":[159],"best":[161,176],"Experiments":[169],"demonstrate":[170],"our":[172],"method":[173],"gives":[174],"than":[178],"other":[179],"state-of-the-art":[180],"approaches.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}