{"id":"https://openalex.org/W4406328634","doi":"https://doi.org/10.1145/3706890.3707009","title":"A SFI-Driven Rapid Distribution Alignment for Classification of Small Sample Data: A Case Study in Adrenal Tumor Analysis","display_name":"A SFI-Driven Rapid Distribution Alignment for Classification of Small Sample Data: A Case Study in Adrenal Tumor Analysis","publication_year":2024,"publication_date":"2024-08-13","ids":{"openalex":"https://openalex.org/W4406328634","doi":"https://doi.org/10.1145/3706890.3707009"},"language":"en","primary_location":{"id":"doi:10.1145/3706890.3707009","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3706890.3707009","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3706890.3707009?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 5th International Symposium on Artificial Intelligence for Medicine Science","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3706890.3707009?download=true","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102322616","display_name":"Wenyi Yang","orcid":null},"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":true,"raw_author_name":"Wenyi Yang","raw_affiliation_strings":["School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0009-0000-1698-8293","affiliations":[{"raw_affiliation_string":"School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101803551","display_name":"Shengjie Guo","orcid":"https://orcid.org/0000-0001-7953-8775"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I4210146711","display_name":"Sun Yat-sen University Cancer Center","ror":"https://ror.org/0400g8r85","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210146711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengjie Guo","raw_affiliation_strings":["Department of Urology, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0001-7953-8775","affiliations":[{"raw_affiliation_string":"Department of Urology, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I157773358","https://openalex.org/I4210146711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100673448","display_name":"Bin Li","orcid":"https://orcid.org/0000-0001-6767-5350"},"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 Li","raw_affiliation_strings":["School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0001-6767-5350","affiliations":[{"raw_affiliation_string":"School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011001803","display_name":"Huangyuan Wu","orcid":"https://orcid.org/0000-0002-7063-3917"},"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":"Huangyuan Wu","raw_affiliation_strings":["School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0002-7063-3917","affiliations":[{"raw_affiliation_string":"School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102322616"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22728411,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"695","last_page":"700"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9919999837875366,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9919999837875366,"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/T10862","display_name":"AI in cancer detection","score":0.9825999736785889,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9638000130653381,"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/sample","display_name":"Sample (material)","score":0.5896773338317871},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5259193181991577},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.055618464946746826}],"concepts":[{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5896773338317871},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5259193181991577},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.055618464946746826},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3706890.3707009","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3706890.3707009","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3706890.3707009?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 5th International Symposium on Artificial Intelligence for Medicine Science","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3706890.3707009","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3706890.3707009","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3706890.3707009?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 5th International Symposium on Artificial Intelligence for Medicine Science","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4406328634.pdf","grobid_xml":"https://content.openalex.org/works/W4406328634.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W2025653905","https://openalex.org/W2165698076","https://openalex.org/W2413867752","https://openalex.org/W2533800772","https://openalex.org/W2592929672","https://openalex.org/W2767128594","https://openalex.org/W2786808285","https://openalex.org/W2906598409","https://openalex.org/W2966036416","https://openalex.org/W2976933962","https://openalex.org/W2984472267","https://openalex.org/W3090414386","https://openalex.org/W4238893454","https://openalex.org/W4292387244","https://openalex.org/W4403746636","https://openalex.org/W6605886079","https://openalex.org/W6610754620","https://openalex.org/W6676840641","https://openalex.org/W6732582004","https://openalex.org/W6765451912","https://openalex.org/W6784204135"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"The":[0,185],"disparate":[1,45],"distribution":[2,91,152],"of":[3,15,55,92,134,181,187,196],"data":[4,21,47,127,136,151],"across":[5,44,172],"medical":[6,24],"centers":[7],"represents":[8],"a":[9,67,82,130,200],"significant":[10],"challenge":[11],"to":[12,40,51,87,122,149,158],"the":[13,23,56,59,90,93,97,105,110,114,124,135,179,194],"development":[14],"generalized":[16],"machine":[17],"learning":[18],"models.":[19],"Furthermore,":[20],"in":[22,58,169,183,199],"field":[25],"are":[26,147],"typically":[27],"limited":[28],"and":[29,96,128,145],"imbalanced,":[30],"making":[31],"it":[32],"more":[33,131],"difficult":[34],"for":[35,77],"traditional":[36,160],"domain":[37,95,99,161],"adaptation":[38,162],"methods":[39],"learn":[41],"shared":[42],"knowledge":[43],"hospital":[46],"sets,":[48],"further":[49],"leading":[50],"poor":[52],"generalization":[53],"ability":[54],"model":[57],"target":[60,98],"domain.":[61],"In":[62,156],"this":[63],"paper,":[64],"we":[65],"propose":[66],"SFI":[68],"(Shapley-based":[69],"Feature":[70],"Importance)":[71],"Driven":[72],"Rapid":[73],"Distribution":[74],"Alignment":[75],"method":[76,189],"classification,":[78],"aimed":[79],"at":[80],"identifying":[81],"potential":[83],"low-dimensional":[84],"feature":[85,106,139,170],"space":[86],"rapidly":[88],"align":[89,150],"source":[94],"on":[100,142],"small":[101,125,201],"sample":[102,126],"data.":[103],"First,":[104],"importance":[107],"calculated":[108],"from":[109,153],"Shapely":[111,143],"value":[112],"guides":[113],"Conditional":[115],"Tabular":[116],"Generative":[117],"Adversarial":[118],"Network":[119],"(CTGAN)":[120],"algorithm":[121],"augment":[123],"provide":[129],"precise":[132],"characterization":[133],"distribution.":[137],"Then,":[138],"elimination":[140],"based":[141],"values":[144],"harmony":[146],"combined":[148],"different":[154],"hospitals.":[155],"contrast":[157],"existing":[159],"techniques":[163],"that":[164],"solely":[165],"prioritize":[166],"reducing":[167],"discrepancies":[168],"distributions":[171],"diverse":[173],"domains,":[174],"our":[175,188],"approach":[176],"additionally":[177],"considers":[178],"significance":[180],"features":[182],"classification.":[184],"effectiveness":[186],"was":[190],"finally":[191],"verified":[192],"through":[193],"classification":[195],"adrenal":[197],"tumors":[198],"multi-center":[202],"sample.":[203]},"counts_by_year":[],"updated_date":"2026-03-09T07:00:12.390032","created_date":"2025-10-10T00:00:00"}