{"id":"https://openalex.org/W4242623661","doi":"https://doi.org/10.1080/21681163.2015.1135299","title":"Deep similarity learning for multimodal medical images","display_name":"Deep similarity learning for multimodal medical images","publication_year":2016,"publication_date":"2016-04-06","ids":{"openalex":"https://openalex.org/W4242623661","doi":"https://doi.org/10.1080/21681163.2015.1135299"},"language":"en","primary_location":{"id":"doi:10.1080/21681163.2015.1135299","is_oa":false,"landing_page_url":"https://doi.org/10.1080/21681163.2015.1135299","pdf_url":null,"source":{"id":"https://openalex.org/S2764763012","display_name":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization","issn_l":"2168-1163","issn":["2168-1163","2168-1171"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging &amp; Visualization","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"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/A5058821252","display_name":"Xi Cheng","orcid":"https://orcid.org/0000-0002-2547-2800"},"institutions":[{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xi Cheng","raw_affiliation_strings":["Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, USA","institution_ids":["https://openalex.org/I4210151799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100408266","display_name":"Li Zhang","orcid":"https://orcid.org/0009-0003-0682-4027"},"institutions":[{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Li Zhang","raw_affiliation_strings":["Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, USA","institution_ids":["https://openalex.org/I4210151799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051649145","display_name":"Yefeng Zheng","orcid":"https://orcid.org/0000-0003-2195-2847"},"institutions":[{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yefeng Zheng","raw_affiliation_strings":["Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, USA","institution_ids":["https://openalex.org/I4210151799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100408266"],"corresponding_institution_ids":["https://openalex.org/I4210151799"],"apc_list":null,"apc_paid":null,"fwci":9.6885,"has_fulltext":false,"cited_by_count":159,"citation_normalized_percentile":{"value":0.98323477,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"6","issue":"3","first_page":"248","last_page":"252"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9991000294685364,"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"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9950000047683716,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7926942110061646},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7174070477485657},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5804414749145508},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5782530307769775},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5643746852874756},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.552912712097168},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.46511510014533997},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4512809216976166},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.4287049174308777},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.26306861639022827}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7926942110061646},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7174070477485657},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5804414749145508},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5782530307769775},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5643746852874756},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.552912712097168},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.46511510014533997},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4512809216976166},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.4287049174308777},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.26306861639022827},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/21681163.2015.1135299","is_oa":false,"landing_page_url":"https://doi.org/10.1080/21681163.2015.1135299","pdf_url":null,"source":{"id":"https://openalex.org/S2764763012","display_name":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization","issn_l":"2168-1163","issn":["2168-1163","2168-1171"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging &amp; Visualization","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2105456967","https://openalex.org/W2126598020","https://openalex.org/W2187281534","https://openalex.org/W2596356468"],"related_works":["https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W2319693127","https://openalex.org/W2474567666","https://openalex.org/W2072263576","https://openalex.org/W2790658443","https://openalex.org/W1940044583","https://openalex.org/W2056226831","https://openalex.org/W2806903871","https://openalex.org/W4320802053"],"abstract_inverted_index":{"An":[0],"effective":[1],"similarity":[2,38,53,83,165,181],"measure":[3],"for":[4,9,175],"multi-modal":[5,90,142],"images":[6],"is":[7,22,72,124],"crucial":[8],"medical":[10],"image":[11,67,117],"fusion":[12],"in":[13,42],"many":[14],"clinical":[15],"applications.":[16],"The":[17,69,138],"underlining":[18],"correlation":[19],"across":[20],"modalities":[21],"usually":[23],"too":[24],"complex":[25],"to":[26,61,74,88,94],"be":[27],"modelled":[28],"by":[29],"intensity-based":[30],"statistical":[31],"metrics.":[32],"Therefore,":[33],"approaches":[34],"of":[35,65,140,150,171],"learning":[36,54],"a":[37,50,58,75,120,176],"metric":[39,107,174],"are":[40,154],"proposed":[41,106,173],"recent":[43],"years.":[44],"In":[45],"this":[46],"work,":[47],"we":[48,86],"propose":[49,87],"novel":[51],"deep":[52,98,148],"method":[55],"that":[56],"trains":[57],"binary":[59],"classifier":[60],"learn":[62],"the":[63,82,97,105,141,147,151,158,164,169,172],"correspondence":[64],"two":[66,127],"patches.":[68],"classification":[70],"output":[71],"transformed":[73],"continuous":[76],"probability":[77],"value,":[78],"then":[79],"used":[80,129],"as":[81],"score.":[84],"Moreover,":[85],"utilise":[89],"stacked":[91,143],"denoising":[92,144],"autoencoder":[93,145],"effectively":[95],"pre-train":[96],"neural":[99,152],"network.":[100],"We":[101],"train":[102],"and":[103,113,134,146,160,179],"test":[104],"using":[108],"sampled":[109],"corresponding/non-corresponding":[110],"computed":[111],"tomography":[112],"magnetic":[114],"resonance":[115],"head":[116],"patches":[118],"from":[119,163],"same":[121],"subject.":[122],"Comparison":[123],"made":[125],"with":[126],"commonly":[128],"metrics:":[130],"normalised":[131],"mutual":[132],"information":[133],"local":[135],"cross":[136],"correlation.":[137],"contributions":[139],"structure":[149],"network":[153],"also":[155],"evaluated.":[156],"Both":[157],"quantitative":[159],"qualitative":[161],"results":[162],"ranking":[166],"experiments":[167],"show":[168],"advantage":[170],"highly":[177],"accurate":[178],"robust":[180],"measure.":[182]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":23},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":31},{"year":2020,"cited_by_count":28},{"year":2019,"cited_by_count":17},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
