{"id":"https://openalex.org/W4294509827","doi":"https://doi.org/10.48550/arxiv.2208.03526","title":"Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification","display_name":"Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification","publication_year":2022,"publication_date":"2022-08-06","ids":{"openalex":"https://openalex.org/W4294509827","doi":"https://doi.org/10.48550/arxiv.2208.03526"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2208.03526","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2208.03526","pdf_url":"https://arxiv.org/pdf/2208.03526","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2208.03526","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101627769","display_name":"Zhikang Wang","orcid":"https://orcid.org/0000-0001-9587-1965"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhikang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112113696","display_name":"Yue Bi","orcid":"https://orcid.org/0009-0004-6187-5641"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bi, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030786536","display_name":"T. Pan","orcid":"https://orcid.org/0000-0002-4700-1516"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Tong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357613","display_name":"Xiaoyu Wang","orcid":"https://orcid.org/0000-0002-6431-8822"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xiaoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111557120","display_name":"Chris Bain","orcid":"https://orcid.org/0009-0001-1786-2956"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bain, Chris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056493973","display_name":"Richard Bassed","orcid":"https://orcid.org/0000-0001-5473-055X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bassed, Richard","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090788230","display_name":"Seiya Imoto","orcid":"https://orcid.org/0000-0002-2989-308X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Imoto, Seiya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100695406","display_name":"Jianhua Yao","orcid":"https://orcid.org/0000-0001-9157-9596"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Jianhua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5012709797","display_name":"Jiangning Song","orcid":"https://orcid.org/0000-0001-8031-9086"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Jiangning","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9959999918937683,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9959999918937683,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9462000131607056,"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":0.9333000183105469,"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/computer-science","display_name":"Computer science","score":0.7790604829788208},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6651175022125244},{"id":"https://openalex.org/keywords/multiplex","display_name":"Multiplex","score":0.6191182732582092},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6031612157821655},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5464474558830261},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5126587748527527},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4425169825553894},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4423461854457855},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33222872018814087},{"id":"https://openalex.org/keywords/bioinformatics","display_name":"Bioinformatics","score":0.07390019297599792}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7790604829788208},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6651175022125244},{"id":"https://openalex.org/C2781188995","wikidata":"https://www.wikidata.org/wiki/Q6934982","display_name":"Multiplex","level":2,"score":0.6191182732582092},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6031612157821655},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5464474558830261},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5126587748527527},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4425169825553894},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4423461854457855},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33222872018814087},{"id":"https://openalex.org/C60644358","wikidata":"https://www.wikidata.org/wiki/Q128570","display_name":"Bioinformatics","level":1,"score":0.07390019297599792},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2208.03526","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2208.03526","pdf_url":"https://arxiv.org/pdf/2208.03526","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"},{"id":"doi:10.48550/arxiv.2208.03526","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2208.03526","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2208.03526","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2208.03526","pdf_url":"https://arxiv.org/pdf/2208.03526","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3006199032","https://openalex.org/W1963826946","https://openalex.org/W2087421209","https://openalex.org/W2547948671","https://openalex.org/W4281783318","https://openalex.org/W2023853334","https://openalex.org/W2001010891","https://openalex.org/W2565428513","https://openalex.org/W3135623036","https://openalex.org/W3174010013"],"abstract_inverted_index":{"Multiple":[0],"instance":[1,69],"learning":[2,70],"(MIL)":[3],"is":[4,25,79,197],"a":[5,65,192,204],"powerful":[6],"approach":[7],"to":[8,26,72,141,177],"classify":[9],"whole":[10],"slide":[11],"images":[12],"(WSIs)":[13],"for":[14,116,146,199],"diagnostic":[15],"pathology.":[16],"A":[17],"fundamental":[18],"challenge":[19],"of":[20,58,108,234],"MIL":[21],"on":[22,215],"WSI":[23,200],"classification":[24,201],"discover":[27],"the":[28,33,43,52,74,82,89,97,106,112,117,126,130,143,147,152,162,169,182,187,232],"\\textit{critical":[29],"instances}":[30],"that":[31],"trigger":[32],"bag":[34],"label.":[35],"However,":[36],"previous":[37],"methods":[38],"are":[39],"primarily":[40],"designed":[41],"under":[42],"independent":[44],"and":[45,88,94,110,134,154,166,185,223,229],"identical":[46],"distribution":[47,127],"hypothesis":[48],"(\\textit{i.i.d}),":[49],"ignoring":[50],"either":[51],"correlations":[53],"between":[54,164],"instances":[55,109,165],"or":[56],"heterogeneity":[57],"tumours.":[59,174],"In":[60,149],"this":[61,150],"paper,":[62],"we":[63,190],"propose":[64],"novel":[66],"multiplex-detection-based":[67],"multiple":[68],"(MDMIL)":[71],"tackle":[73],"issues":[75],"above.":[76],"Specifically,":[77],"MDMIL":[78,237],"constructed":[80],"by":[81,96,120],"internal":[83,113],"query":[84,114,157],"generation":[85],"module":[86,92],"(IQGM)":[87],"multiplex":[90],"detection":[91],"(MDM)":[93],"assisted":[95],"memory-based":[98,193],"contrastive":[99,194],"loss":[100],"during":[101],"training.":[102],"Firstly,":[103],"IQGM":[104],"gives":[105],"probability":[107],"generates":[111],"(IQ)":[115],"subsequent":[118],"MDM":[119,139],"aggregating":[121],"highly":[122],"reliable":[123],"features":[124],"after":[125],"analysis.":[128],"Secondly,":[129],"multiplex-detection":[131],"cross-attention":[132],"(MDCA)":[133],"multi-head":[135],"self-attention":[136],"(MHSA)":[137],"in":[138,181,209],"cooperate":[140],"generate":[142],"final":[144],"representations":[145],"WSI.":[148],"process,":[151,189],"IQ":[153],"trainable":[155],"variational":[156],"(VQ)":[158],"successfully":[159],"build":[160],"up":[161],"connections":[163],"significantly":[167],"improve":[168],"model's":[170],"robustness":[171],"toward":[172],"heterogeneous":[173],"At":[175],"last,":[176],"further":[178],"enforce":[179],"constraints":[180],"feature":[183],"space":[184],"stabilize":[186],"training":[188],"adopt":[191],"loss,":[195],"which":[196],"practicable":[198],"even":[202],"with":[203],"single":[205],"sample":[206],"as":[207],"input":[208],"each":[210],"iteration.":[211],"We":[212],"conduct":[213],"experiments":[214],"three":[216],"computational":[217],"pathology":[218],"datasets,":[219],"e.g.,":[220],"CAMELYON16,":[221],"TCGA-NSCLC,":[222],"TCGA-RCC":[224],"datasets.":[225],"The":[226],"superior":[227],"accuracy":[228],"AUC":[230],"demonstrate":[231],"superiority":[233],"our":[235],"proposed":[236],"over":[238],"other":[239],"state-of-the-art":[240],"methods.":[241]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2022-09-03T00:00:00"}