{"id":"https://openalex.org/W4386352634","doi":"https://doi.org/10.1109/isbi53787.2023.10230341","title":"A Global-Local Features Exchange and Fusion Network for Multi-Organ Segmentation","display_name":"A Global-Local Features Exchange and Fusion Network for Multi-Organ Segmentation","publication_year":2023,"publication_date":"2023-04-18","ids":{"openalex":"https://openalex.org/W4386352634","doi":"https://doi.org/10.1109/isbi53787.2023.10230341"},"language":"en","primary_location":{"id":"doi:10.1109/isbi53787.2023.10230341","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isbi53787.2023.10230341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5060376841","display_name":"Zongyu Li","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongyu Li","raw_affiliation_strings":["Beijing Institute of Technology,School of Medical Technology,Beijing,China,100081"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Medical Technology,Beijing,China,100081","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066874145","display_name":"Yucong Lin","orcid":"https://orcid.org/0000-0002-9039-0318"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yucong Lin","raw_affiliation_strings":["Beijing Institute of Technology,School of Medical Technology,Beijing,China,100081"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Medical Technology,Beijing,China,100081","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012390446","display_name":"Danni Ai","orcid":"https://orcid.org/0000-0002-2285-0570"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Danni Ai","raw_affiliation_strings":["Beijing Institute of Technology,Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display,School of Optics and Photonics,Beijing,China,100081"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display,School of Optics and Photonics,Beijing,China,100081","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100614247","display_name":"Jian Yang","orcid":"https://orcid.org/0000-0003-2373-8799"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Yang","raw_affiliation_strings":["Beijing Institute of Technology,Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display,School of Optics and Photonics,Beijing,China,100081"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display,School of Optics and Photonics,Beijing,China,100081","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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/T10862","display_name":"AI in cancer detection","score":0.9966999888420105,"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"}},{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9951000213623047,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7929626703262329},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.741997480392456},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6684526205062866},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6488659977912903},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6050394773483276},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5813411474227905},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.579160749912262},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47174251079559326},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4347642958164215},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.43008702993392944},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0786348283290863},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.06791961193084717}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7929626703262329},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.741997480392456},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6684526205062866},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6488659977912903},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6050394773483276},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5813411474227905},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.579160749912262},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47174251079559326},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4347642958164215},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.43008702993392944},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0786348283290863},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.06791961193084717},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi53787.2023.10230341","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isbi53787.2023.10230341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2962914239","https://openalex.org/W3014974815","https://openalex.org/W3094502228","https://openalex.org/W3112701542","https://openalex.org/W3127751679","https://openalex.org/W3132455321","https://openalex.org/W3138516171","https://openalex.org/W3160284783","https://openalex.org/W3160694286","https://openalex.org/W3197957534","https://openalex.org/W3198035652","https://openalex.org/W3204166336","https://openalex.org/W3204255739","https://openalex.org/W4224979365","https://openalex.org/W4225016626","https://openalex.org/W4281631732","https://openalex.org/W6639824700","https://openalex.org/W6784333009","https://openalex.org/W6790275670","https://openalex.org/W6791469159","https://openalex.org/W6795435739","https://openalex.org/W6801350836"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W4390516098","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"network(CNN)":[2],"based":[3,129],"methods":[4],"for":[5,40],"multi-organ":[6,42],"segmentation":[7],"have":[8],"achieved":[9],"impressive":[10],"results.":[11],"However,":[12],"the":[13,50,53,60,81,85,90,112,116,121,124,131],"global":[14,63],"feature":[15,64,72,99,104],"extraction":[16,65],"capability":[17],"of":[18,59,67,80,115,123],"CNNs":[19],"is":[20,47],"limited":[21],"due":[22],"to":[23,83,110,119],"their":[24],"localisation":[25],"problem.":[26],"In":[27,96],"this":[28],"paper,":[29],"we":[30],"propose":[31],"a":[32,98,103],"more":[33],"efficient":[34],"CNN":[35,51],"and":[36,52,62,88,102,145],"Transformer":[37,54],"hybrid":[38],"network":[39],"abdominal":[41],"segmentation.":[43],"A":[44],"parallel":[45],"encoder":[46,82,94],"formed":[48],"by":[49],"encoder,":[55],"making":[56],"full":[57],"use":[58],"local":[61],"capabilities":[66],"both.":[68],"Based":[69],"on":[70,130],"this,":[71],"exchange":[73],"modules":[74],"are":[75,108],"inserted":[76],"at":[77],"each":[78],"scale":[79],"enhance":[84],"features":[86,114],"flow":[87],"alleviate":[89],"variability":[91],"between":[92],"different":[93],"features.":[95,127],"addition,":[97],"fusion":[100],"module":[101],"consistency":[105,122],"loss":[106],"function":[107],"proposed":[109],"couple":[111],"output":[113],"two":[117],"encoders":[118],"ensure":[120],"decoder":[125],"input":[126],"Experiments":[128],"Synapse":[132],"dataset":[133],"show":[134],"that":[135],"our":[136],"approach":[137],"achieves":[138],"superior":[139],"results":[140],"compared":[141],"with":[142],"both":[143],"CNN-based":[144],"Transformer-based":[146],"state-of-the-art":[147],"methods.":[148]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
