{"id":"https://openalex.org/W7131399301","doi":"https://doi.org/10.48550/arxiv.2602.20187","title":"AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image","display_name":"AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image","publication_year":2026,"publication_date":"2026-02-21","ids":{"openalex":"https://openalex.org/W7131399301","doi":"https://doi.org/10.48550/arxiv.2602.20187"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.20187","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126812408","display_name":"Tingting Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zheng, Tingting","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126800739","display_name":"Hongxun Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Hongxun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126784474","display_name":"Kui Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Kui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhao, Sicheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Sicheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126810027","display_name":"Yi Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Yi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5126812408"],"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":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.8503999710083008,"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":0.8503999710083008,"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":0.03550000116229057,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.025299999862909317,"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/discriminative-model","display_name":"Discriminative model","score":0.8062999844551086},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.6057999730110168},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4625999927520752},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.44780001044273376},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.4052000045776367},{"id":"https://openalex.org/keywords/completeness","display_name":"Completeness (order theory)","score":0.39320001006126404}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8062999844551086},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7468000054359436},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.6057999730110168},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5888000130653381},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4625999927520752},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.44780001044273376},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.4052000045776367},{"id":"https://openalex.org/C17231256","wikidata":"https://www.wikidata.org/wiki/Q5156540","display_name":"Completeness (order theory)","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3384000062942505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3343999981880188},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.274399995803833},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.25029999017715454}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.20187","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.20187","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20187","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.20187","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.7919645309448242,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,10,35,110],"multi-instance":[3],"learning":[4],"(MIL)":[5],"have":[6],"witnessed":[7],"impressive":[8],"performance":[9],"whole":[11],"slide":[12],"image":[13],"(WSI)":[14],"analysis.":[15],"However,":[16],"the":[17,68,106,141],"inherent":[18],"sparsity":[19],"of":[20,49,56],"tumors":[21],"and":[22,38,65,122,129,155,170,178,183,187],"their":[23,62,198],"morphological":[24],"diversity":[25],"lead":[26],"to":[27,78,99,119,126,147],"obvious":[28],"heterogeneity":[29],"across":[30,81],"regions,":[31,82],"posing":[32],"significant":[33],"challenges":[34],"aggregating":[36],"high-quality":[37],"discriminative":[39,66],"representations.":[40],"To":[41],"address":[42],"this,":[43],"we":[44,91,131,157],"introduce":[45],"a":[46,53,93,159,167],"novel":[47],"concept":[48],"anchor":[50,95],"instance":[51],"(AI),":[52],"compact":[54],"subset":[55],"instances":[57],"that":[58,139],"are":[59,185],"representative":[60],"within":[61],"regions":[63,146],"(local)":[64],"at":[67],"bag":[69],"(global)":[70],"level.":[71],"These":[72],"AIs":[73,101],"act":[74],"as":[75],"semantic":[76],"references":[77],"guide":[79],"interactions":[80],"correcting":[83],"non-discriminative":[84],"patterns":[85],"while":[86],"preserving":[87],"regional":[88,150],"diversity.":[89],"Specifically,":[90],"propose":[92],"dual-level":[94],"mining":[96],"(DAM)":[97],"module":[98,138],"\\textbf{select}":[100],"from":[102,144],"massive":[103],"instances,":[104],"where":[105],"most":[107],"informative":[108],"AI":[109],"each":[111,149],"region":[112,135],"is":[113],"extracted":[114],"by":[115],"assessing":[116],"its":[117],"similarity":[118],"both":[120,181],"local":[121],"global":[123],"embeddings.":[124],"Furthermore,":[125],"ensure":[127],"completeness":[128],"diversity,":[130],"devise":[132],"an":[133],"anchor-guided":[134],"correction":[136],"(ARC)":[137],"explores":[140],"complementary":[142],"information":[143],"all":[145],"\\textbf{correct}":[148],"representation.":[151],"Building":[152],"upon":[153],"DAM":[154,182],"ARC,":[156],"develop":[158],"concise":[160],"yet":[161],"effective":[162],"framework,":[163],"AINet,":[164],"which":[165],"employs":[166],"simple":[168],"predictor":[169],"surpasses":[171],"state-of-the-art":[172],"methods":[173],"with":[174],"substantially":[175],"fewer":[176],"FLOPs":[177],"parameters.":[179],"Moreover,":[180],"ARC":[184],"modular":[186],"can":[188],"be":[189],"seamlessly":[190],"integrated":[191],"into":[192],"existing":[193],"MIL":[194],"frameworks,":[195],"consistently":[196],"improving":[197],"performance.":[199]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-26T00:00:00"}