{"id":"https://openalex.org/W6891851540","doi":"https://doi.org/10.48550/arxiv.2407.17267","title":"M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysis","display_name":"M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysis","publication_year":2024,"publication_date":"2024-07-24","ids":{"openalex":"https://openalex.org/W6891851540","doi":"https://doi.org/10.48550/arxiv.2407.17267"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2407.17267","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.17267","pdf_url":"https://arxiv.org/pdf/2407.17267","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":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2407.17267","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Li, Junyu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Junyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhang, Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ye","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Shu, Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shu, Wen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Feng, Xiaobing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Xiaobing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Yingchun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yingchun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yan, Pengju","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Pengju","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Li, Xiaolin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiaolin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Sha, Chulin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sha, Chulin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"He, Min","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Min","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9782000184059143,"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":0.9782000184059143,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.004999999888241291,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.002199999988079071,"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/subtyping","display_name":"Subtyping","score":0.6301000118255615},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5436000227928162},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45559999346733093},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.4311000108718872},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.429500013589859},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.41530001163482666},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4059000015258789},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.39800000190734863}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7544999718666077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6995999813079834},{"id":"https://openalex.org/C83852419","wikidata":"https://www.wikidata.org/wiki/Q2713292","display_name":"Subtyping","level":2,"score":0.6301000118255615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6104000210762024},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5436000227928162},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.4311000108718872},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.429500013589859},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4259999990463257},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.41530001163482666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4059000015258789},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.37049999833106995},{"id":"https://openalex.org/C501734568","wikidata":"https://www.wikidata.org/wiki/Q42918","display_name":"Mutation","level":3,"score":0.36719998717308044},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2890999913215637},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2621999979019165}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2407.17267","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.17267","pdf_url":"https://arxiv.org/pdf/2407.17267","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":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2407.17267","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2407.17267","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2407.17267","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.17267","pdf_url":"https://arxiv.org/pdf/2407.17267","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":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multiple":[0,72],"instance":[1,73],"learning":[2,74],"(MIL)":[3],"has":[4,93],"been":[5],"successfully":[6],"applied":[7,77],"for":[8,71,80,107,124],"whole":[9],"slide":[10],"images":[11],"(WSIs)":[12],"analysis":[13],"in":[14,44,143],"computational":[15],"pathology,":[16],"enabling":[17],"a":[18,99,112],"wide":[19],"range":[20],"of":[21,54,66,83,101,129],"prediction":[22,82,110],"tasks":[23],"from":[24,87],"tumor":[25],"subtyping":[26],"to":[27,145],"inferring":[28],"genetic":[29,85],"mutations":[30,86],"and":[31,76,121,126],"multi-omics":[32],"biomarkers.":[33],"However,":[34],"existing":[35],"MIL":[36],"methods":[37],"predominantly":[38],"focus":[39],"on":[40,111],"single-task":[41,148],"learning,":[42],"resulting":[43],"not":[45],"only":[46],"overall":[47],"low":[48],"efficiency":[49],"but":[50],"also":[51],"the":[52],"overlook":[53],"inter-task":[55],"relatedness.":[56],"To":[57],"address":[58],"these":[59],"issues,":[60],"we":[61],"proposed":[62,90],"an":[63],"adapted":[64],"architecture":[65],"Multi-gate":[67],"Mixture-of-experts":[68],"with":[69,103],"Multi-proxy":[70],"(M4),":[75],"this":[78],"framework":[79],"simultaneous":[81],"multiple":[84,104],"WSIs.":[88],"The":[89,150],"M4":[91],"model":[92,134],"two":[94],"main":[95],"innovations:":[96],"(1)":[97],"utilizing":[98],"mixture":[100],"experts":[102],"gating":[105],"strategies":[106],"multi-genetic":[108],"mutation":[109],"single":[113],"pathological":[114,130],"slide;":[115],"(2)":[116],"constructing":[117],"multi-proxy":[118],"expert":[119],"network":[120,123],"gate":[122],"comprehensive":[125],"effective":[127],"modeling":[128],"image":[131],"information.":[132],"Our":[133],"achieved":[135],"significant":[136],"improvements":[137],"across":[138],"five":[139],"tested":[140],"TCGA":[141],"datasets":[142],"comparison":[144],"current":[146],"state-of-the-art":[147],"methods.":[149],"code":[151],"is":[152],"available":[153],"at:https://github.com/Bigyehahaha/M4.":[154]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
