{"id":"https://openalex.org/W3214890555","doi":"https://doi.org/10.1109/tmi.2021.3131245","title":"Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation","display_name":"Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation","publication_year":2021,"publication_date":"2021-11-29","ids":{"openalex":"https://openalex.org/W3214890555","doi":"https://doi.org/10.1109/tmi.2021.3131245","mag":"3214890555","pmid":"https://pubmed.ncbi.nlm.nih.gov/34843432"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2021.3131245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2021.3131245","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"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 Medical Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2203.10332","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5056289980","display_name":"Cheng Bian","orcid":"https://orcid.org/0000-0003-3498-8283"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Cheng Bian","raw_affiliation_strings":["Tencent Jarvis Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-3498-8283","affiliations":[{"raw_affiliation_string":"Tencent Jarvis Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102022220","display_name":"Chenglang Yuan","orcid":"https://orcid.org/0000-0002-4125-4993"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglang Yuan","raw_affiliation_strings":["Tencent Jarvis Laboratory, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Jarvis Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100621577","display_name":"Kai Ma","orcid":"https://orcid.org/0000-0003-2805-3692"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Ma","raw_affiliation_strings":["Tencent Jarvis Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-2805-3692","affiliations":[{"raw_affiliation_string":"Tencent Jarvis Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006758379","display_name":"Shuang Yu","orcid":"https://orcid.org/0000-0002-4022-6819"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuang Yu","raw_affiliation_strings":["Tencent Jarvis Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-4022-6819","affiliations":[{"raw_affiliation_string":"Tencent Jarvis Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101459790","display_name":"Dong Wei","orcid":"https://orcid.org/0000-0001-5969-6987"},"institutions":[{"id":"https://openalex.org/I4210103558","display_name":"Tencent Healthcare (China)","ror":"https://ror.org/019xckf23","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Wei","raw_affiliation_strings":["Tencent Healthcare (Shenzhen) Co., Ltd., Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-5969-6987","affiliations":[{"raw_affiliation_string":"Tencent Healthcare (Shenzhen) Co., Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210103558"]}]},{"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/I4210103558","display_name":"Tencent Healthcare (China)","ror":"https://ror.org/019xckf23","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yefeng Zheng","raw_affiliation_strings":["Tencent Healthcare (Shenzhen) Co., Ltd., Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-2195-2847","affiliations":[{"raw_affiliation_string":"Tencent Healthcare (Shenzhen) Co., Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210103558"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5056289980"],"corresponding_institution_ids":["https://openalex.org/I2250653659"],"apc_list":null,"apc_paid":null,"fwci":4.3395,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.95249815,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"41","issue":"5","first_page":"1043","last_page":"1056"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9987000226974487,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9937000274658203,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9864000082015991,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7682989835739136},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6891472935676575},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.6515403985977173},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.6096471548080444},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5260089635848999},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5187749862670898},{"id":"https://openalex.org/keywords/terminology","display_name":"Terminology","score":0.5011613368988037},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.49097660183906555},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.42851993441581726},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.41070228815078735},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41054603457450867},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.4103030264377594},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.33119577169418335},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32897835969924927},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.20466086268424988}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7682989835739136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6891472935676575},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.6515403985977173},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.6096471548080444},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5260089635848999},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5187749862670898},{"id":"https://openalex.org/C547195049","wikidata":"https://www.wikidata.org/wiki/Q1725664","display_name":"Terminology","level":2,"score":0.5011613368988037},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.49097660183906555},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.42851993441581726},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.41070228815078735},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41054603457450867},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.4103030264377594},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33119577169418335},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32897835969924927},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.20466086268424988},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006321","descriptor_name":"Heart","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D006321","descriptor_name":"Heart","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D006321","descriptor_name":"Heart","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D030541","descriptor_name":"Databases, Genetic","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D030541","descriptor_name":"Databases, Genetic","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D030541","descriptor_name":"Databases, Genetic","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1109/tmi.2021.3131245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2021.3131245","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"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 Medical Imaging","raw_type":"journal-article"},{"id":"pmid:34843432","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34843432","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on medical imaging","raw_type":null},{"id":"pmh:oai:arXiv.org:2203.10332","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.10332","pdf_url":"https://arxiv.org/pdf/2203.10332","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2203.10332","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.10332","pdf_url":"https://arxiv.org/pdf/2203.10332","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.5699999928474426,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G549130994","display_name":null,"funder_award_id":"2020AAA0109500/2020AAA0109501","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":78,"referenced_works":["https://openalex.org/W1565327149","https://openalex.org/W1682403713","https://openalex.org/W1901129140","https://openalex.org/W2036963181","https://openalex.org/W2081580037","https://openalex.org/W2116447720","https://openalex.org/W2128532956","https://openalex.org/W2134270519","https://openalex.org/W2140679639","https://openalex.org/W2141350700","https://openalex.org/W2159291411","https://openalex.org/W2250539671","https://openalex.org/W2250646737","https://openalex.org/W2291593693","https://openalex.org/W2294130536","https://openalex.org/W2560023338","https://openalex.org/W2560647685","https://openalex.org/W2562354316","https://openalex.org/W2562469482","https://openalex.org/W2593768305","https://openalex.org/W2620487580","https://openalex.org/W2630837129","https://openalex.org/W2752782242","https://openalex.org/W2798892366","https://openalex.org/W2884585870","https://openalex.org/W2891179298","https://openalex.org/W2899867883","https://openalex.org/W2915496375","https://openalex.org/W2924464923","https://openalex.org/W2924485953","https://openalex.org/W2932414082","https://openalex.org/W2936111010","https://openalex.org/W2949477454","https://openalex.org/W2955058313","https://openalex.org/W2962793481","https://openalex.org/W2962825119","https://openalex.org/W2963073614","https://openalex.org/W2963107255","https://openalex.org/W2963486920","https://openalex.org/W2963797156","https://openalex.org/W2963936013","https://openalex.org/W2963955422","https://openalex.org/W2964090697","https://openalex.org/W2964278684","https://openalex.org/W2964288524","https://openalex.org/W2969893028","https://openalex.org/W2971113548","https://openalex.org/W2972285644","https://openalex.org/W2981433975","https://openalex.org/W2981874246","https://openalex.org/W2989779801","https://openalex.org/W2994827984","https://openalex.org/W2997957041","https://openalex.org/W2998704965","https://openalex.org/W3002569343","https://openalex.org/W3003733889","https://openalex.org/W3006040295","https://openalex.org/W3027914507","https://openalex.org/W3034359780","https://openalex.org/W3034435444","https://openalex.org/W3034441884","https://openalex.org/W3102255912","https://openalex.org/W3103938557","https://openalex.org/W3143107425","https://openalex.org/W3165446958","https://openalex.org/W3173761247","https://openalex.org/W4233424333","https://openalex.org/W4294170691","https://openalex.org/W4309845474","https://openalex.org/W4386506836","https://openalex.org/W6633949838","https://openalex.org/W6680532216","https://openalex.org/W6682691769","https://openalex.org/W6683633756","https://openalex.org/W6713955831","https://openalex.org/W6760897771","https://openalex.org/W6763578275","https://openalex.org/W6767354860"],"related_works":["https://openalex.org/W2350593162","https://openalex.org/W2390350206","https://openalex.org/W1969477129","https://openalex.org/W2921208823","https://openalex.org/W2353483812","https://openalex.org/W2131808775","https://openalex.org/W2130038259","https://openalex.org/W2360756181","https://openalex.org/W2374093222","https://openalex.org/W2359533638"],"abstract_inverted_index":{"Due":[0],"to":[1,30,38,54,63,71,86,144,148,160,182,187],"the":[2,10,14,32,36,65,75,91,117,123,127,134,146,149,162,170,184,189,214,219,222],"lack":[3],"of":[4,13,102,166,221],"properly":[5],"annotated":[6],"medical":[7,72,76,92,106],"data,":[8],"exploring":[9],"generalization":[11],"capability":[12],"deep":[15,33],"model":[16,34,136,164],"is":[17,61,78,83,195],"becoming":[18],"a":[19,99,140,155,203],"public":[20,199],"concern.":[21],"Zero-shot":[22],"learning":[23],"(ZSL)":[24],"has":[25],"emerged":[26],"in":[27,169],"recent":[28],"years":[29],"equip":[31],"with":[35,116],"ability":[37],"recognize":[39],"unseen":[40],"classes.":[41],"However,":[42],"existing":[43],"studies":[44],"mainly":[45],"focus":[46],"on":[47,197],"natural":[48,66],"images,":[49,73],"which":[50],"utilize":[51],"linguistic":[52,88],"models":[53,89],"extract":[55,122],"auxiliary":[56],"information":[57,167],"for":[58,90,105],"ZSL.":[59],"It":[60],"impractical":[62],"apply":[64],"image":[67],"ZSL":[68,103],"solutions":[69],"directly":[70],"since":[74],"terminology":[77],"very":[79],"domain-specific,":[80],"and":[81,137,206],"it":[82],"not":[84,174],"easy":[85],"acquire":[87],"terminology.":[93],"In":[94],"this":[95],"work,":[96],"we":[97,121,153,176],"propose":[98,139,154],"new":[100],"paradigm":[101],"specifically":[104],"images":[107],"utilizing":[108],"cross-modality":[109,141,200],"information.":[110],"We":[111],"make":[112,161],"three":[113],"main":[114],"contributions":[115],"proposed":[118,193,215],"paradigm.":[119],"First,":[120],"prior":[124,135],"knowledge":[125],"about":[126],"segmentation":[128],"targets,":[129],"called":[130],"relation":[131,156,185],"prototypes,":[132],"from":[133],"then":[138],"adaptation":[142],"module":[143,159,181],"inherit":[145],"prototypes":[147,186],"zero-shot":[150,163],"model.":[151],"Second,":[152],"prototype":[157],"awareness":[158],"aware":[165],"contained":[168],"prototypes.":[171],"Last":[172],"but":[173],"least,":[175],"develop":[177],"an":[178,207],"inheritance":[179,190],"attention":[180],"recalibrate":[183],"enhance":[188],"process.":[191],"The":[192],"framework":[194,216],"evaluated":[196],"two":[198],"datasets":[201],"including":[202],"cardiac":[204],"dataset":[205],"abdominal":[208],"dataset.":[209],"Extensive":[210],"experiments":[211],"show":[212],"that":[213],"significantly":[217],"outperforms":[218],"state":[220],"arts.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":3}],"updated_date":"2026-04-26T08:31:28.666265","created_date":"2025-10-10T00:00:00"}
