{"id":"https://openalex.org/W4393141527","doi":"https://doi.org/10.23919/icact60172.2024.10471995","title":"Overview of the Potentials of Multiple Instance Learning in Cancer Diagnosis: Applications, Challenges, and Future Directions","display_name":"Overview of the Potentials of Multiple Instance Learning in Cancer Diagnosis: Applications, Challenges, and Future Directions","publication_year":2024,"publication_date":"2024-02-04","ids":{"openalex":"https://openalex.org/W4393141527","doi":"https://doi.org/10.23919/icact60172.2024.10471995"},"language":"en","primary_location":{"id":"doi:10.23919/icact60172.2024.10471995","is_oa":false,"landing_page_url":"https://doi.org/10.23919/icact60172.2024.10471995","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 26th International Conference on Advanced Communications Technology (ICACT)","raw_type":"proceedings-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/A5076827703","display_name":"Tagne Poupi Theodore Armand","orcid":"https://orcid.org/0000-0002-5933-3163"},"institutions":[{"id":"https://openalex.org/I104338594","display_name":"Inje University","ror":"https://ror.org/04xqwq985","country_code":"KR","type":"education","lineage":["https://openalex.org/I104338594"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Tagne Poupi Theodore Armand","raw_affiliation_strings":["Institute of Digital Anti-Aging Healthcare, Inje University,Gimhae,Republic of Korea,50834"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Digital Anti-Aging Healthcare, Inje University,Gimhae,Republic of Korea,50834","institution_ids":["https://openalex.org/I104338594"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071495765","display_name":"Subrata Bhattacharjee","orcid":"https://orcid.org/0000-0002-6036-6915"},"institutions":[{"id":"https://openalex.org/I104338594","display_name":"Inje University","ror":"https://ror.org/04xqwq985","country_code":"KR","type":"education","lineage":["https://openalex.org/I104338594"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Subrata Bhattacharjee","raw_affiliation_strings":["u-AHRC, Inje University,Department of Computer Engineering,Gimhae,Republic of Korea,50834"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"u-AHRC, Inje University,Department of Computer Engineering,Gimhae,Republic of Korea,50834","institution_ids":["https://openalex.org/I104338594"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102020002","display_name":"Hee\u2010Cheol Kim","orcid":"https://orcid.org/0000-0002-5399-7647"},"institutions":[{"id":"https://openalex.org/I104338594","display_name":"Inje University","ror":"https://ror.org/04xqwq985","country_code":"KR","type":"education","lineage":["https://openalex.org/I104338594"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hee-Cheol Kim","raw_affiliation_strings":["Institute of Digital Anti-Aging Healthcare, Inje University,Gimhae,Republic of Korea,50834"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Digital Anti-Aging Healthcare, Inje University,Gimhae,Republic of Korea,50834","institution_ids":["https://openalex.org/I104338594"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9164,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.77389131,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"419","last_page":"425"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.7063000202178955,"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/T10862","display_name":"AI in cancer detection","score":0.7063000202178955,"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/T13937","display_name":"Genetics, Bioinformatics, and Biomedical Research","score":0.6779000163078308,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.6565999984741211,"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":0.5878152847290039},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3803217113018036},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36088359355926514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5878152847290039},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3803217113018036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36088359355926514}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/icact60172.2024.10471995","is_oa":false,"landing_page_url":"https://doi.org/10.23919/icact60172.2024.10471995","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 26th International Conference on Advanced Communications Technology (ICACT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2067021570","https://openalex.org/W2098166271","https://openalex.org/W2103243046","https://openalex.org/W2107030642","https://openalex.org/W2110119381","https://openalex.org/W2133288557","https://openalex.org/W2154318594","https://openalex.org/W2327422911","https://openalex.org/W2524399695","https://openalex.org/W2560886373","https://openalex.org/W2715209044","https://openalex.org/W2785934082","https://openalex.org/W2889646458","https://openalex.org/W2894084084","https://openalex.org/W2954253435","https://openalex.org/W2964317695","https://openalex.org/W3022955423","https://openalex.org/W3102161834","https://openalex.org/W3108327761","https://openalex.org/W3126827997","https://openalex.org/W3135547872","https://openalex.org/W3163490502","https://openalex.org/W3176719058","https://openalex.org/W3202630518","https://openalex.org/W3202910984","https://openalex.org/W3215366433","https://openalex.org/W4212862206","https://openalex.org/W4225354217","https://openalex.org/W4280645326","https://openalex.org/W4293051558","https://openalex.org/W4296963672","https://openalex.org/W4309475411","https://openalex.org/W4366779198","https://openalex.org/W4367153932","https://openalex.org/W4380434577","https://openalex.org/W4387744022","https://openalex.org/W6801975214"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"The":[0,38,85],"outcome":[1],"of":[2,40,71,135,137,158,161],"cancer":[3,124,164,196],"patients":[4],"mostly":[5],"depends":[6],"on":[7],"the":[8,12,21,41,46,50,69,91,133,144,159,182],"diagnosis":[9,16],"process":[10],"and":[11,35,56,63,73,81,171,179,199],"treatment":[13],"strategies.":[14],"Computer-aided":[15],"(CAD)":[17],"methods":[18],"have":[19],"demonstrated":[20],"potential":[22],"to":[23,68,94,153,185,187],"handle":[24],"accurate":[25],"diagnostics":[26],"using":[27],"artificial":[28],"intelligence":[29],"techniques":[30],"such":[31],"as":[32,54,113,140,190],"machine":[33],"learning":[34,58,88,109,118,129],"deep":[36],"learning.":[37],"nature":[39],"data":[42],"used":[43,82],"in":[44,120,163,194],"training":[45],"AI-based":[47],"model":[48],"determined":[49],"paradigm,":[51],"often":[52],"classified":[53],"supervised":[55,87,117,128],"unsupervised":[57],"for":[59,83],"scenarios":[60],"with":[61,97],"labeled":[62,80],"unlabeled":[64],"data,":[65],"respectively.":[66],"Due":[67],"cost":[70],"time":[72],"resources,":[74],"most":[75],"datasets":[76],"are":[77],"nowadays":[78],"partially":[79],"training.":[84],"weakly":[86,116],"approach":[89,119],"enables":[90],"AI":[92],"models":[93],"be":[95],"trained":[96],"incompletely":[98],"labeled,":[99],"noisy,":[100],"or":[101],"imbalanced":[102],"data.":[103],"In":[104],"recent":[105],"years,":[106],"multiple":[107],"instance":[108],"(MIL)":[110],"has":[111],"emerged":[112],"a":[114,191],"promising":[115],"many":[121],"fields,":[122],"including":[123],"diagnosis.":[125],"Unlike":[126],"traditional":[127],"methods,":[130],"MIL":[131,162,189],"allows":[132],"classification":[134],"groups":[136],"instances,":[138],"known":[139],"bags,":[141],"where":[142],"only":[143],"bag's":[145],"label":[146],"is":[147],"available.":[148],"This":[149],"comprehensive":[150],"review":[151,183],"aims":[152,184],"provide":[154],"an":[155],"in-depth":[156],"analysis":[157],"applications":[160],"diagnostic":[165,197],"tasks,":[166],"highlighting":[167],"its":[168],"advantages,":[169,177],"challenges,":[170,178],"future":[172,180],"directions.":[173],"By":[174],"examining":[175],"these":[176],"trends,":[181],"contribute":[186],"advancing":[188],"powerful":[192],"tool":[193],"improving":[195],"accuracy":[198],"patient":[200],"outcomes.":[201]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}