{"id":"https://openalex.org/W4403780638","doi":"https://doi.org/10.1145/3664647.3681534","title":"SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification","display_name":"SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403780638","doi":"https://doi.org/10.1145/3664647.3681534"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3681534","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681534","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","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":null,"display_name":"Heng Fang","orcid":"https://orcid.org/0009-0008-7518-2351"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heng Fang","raw_affiliation_strings":["School of Big Data & Software Engineering, Chongqing University, Chongqing, Chongqing, China"],"raw_orcid":"https://orcid.org/0009-0008-7518-2351","affiliations":[{"raw_affiliation_string":"School of Big Data & Software Engineering, Chongqing University, Chongqing, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052560070","display_name":"Sheng Huang","orcid":"https://orcid.org/0000-0001-5610-0826"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Huang","raw_affiliation_strings":["School of Big Data & Software Engineering, Chongqing University, Chongqing, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0001-5610-0826","affiliations":[{"raw_affiliation_string":"School of Big Data & Software Engineering, Chongqing University, Chongqing, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078640314","display_name":"Wenhao Tang","orcid":"https://orcid.org/0000-0002-8835-9215"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhao Tang","raw_affiliation_strings":["School of Big Data & Software Engineering, Chongqing University, Chongqing, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0002-8835-9215","affiliations":[{"raw_affiliation_string":"School of Big Data & Software Engineering, Chongqing University, Chongqing, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117217082","display_name":"Luwen Huangfu","orcid":"https://orcid.org/0000-0003-3926-7901"},"institutions":[{"id":"https://openalex.org/I26538001","display_name":"San Diego State University","ror":"https://ror.org/0264fdx42","country_code":"US","type":"education","lineage":["https://openalex.org/I26538001"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luwen Huangfu","raw_affiliation_strings":["Fowler College of Business, San Diego State University, San Diego, California, USA"],"raw_orcid":"https://orcid.org/0000-0003-3926-7901","affiliations":[{"raw_affiliation_string":"Fowler College of Business, San Diego State University, San Diego, California, USA","institution_ids":["https://openalex.org/I26538001"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100656118","display_name":"Bo Liu","orcid":"https://orcid.org/0000-0002-8678-2271"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Liu","raw_affiliation_strings":["School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China"],"raw_orcid":"https://orcid.org/0000-0002-8678-2271","affiliations":[{"raw_affiliation_string":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China","institution_ids":["https://openalex.org/I16365422"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.2893,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.93158467,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"6083","last_page":"6092"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9987999796867371,"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":0.9987999796867371,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9984999895095825,"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/T10627","display_name":"Advanced Image and Video Retrieval 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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7796018123626709},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6306464672088623},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5749339461326599},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5207933783531189},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48590555787086487},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4181489944458008}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7796018123626709},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6306464672088623},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5749339461326599},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5207933783531189},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48590555787086487},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4181489944458008}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3681534","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681534","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","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":26,"referenced_works":["https://openalex.org/W2302302587","https://openalex.org/W2531897166","https://openalex.org/W2772723798","https://openalex.org/W2956228567","https://openalex.org/W2971376088","https://openalex.org/W3034534840","https://openalex.org/W3089090082","https://openalex.org/W3135547872","https://openalex.org/W3165804767","https://openalex.org/W3176719058","https://openalex.org/W3177618419","https://openalex.org/W4212774754","https://openalex.org/W4283072464","https://openalex.org/W4283801948","https://openalex.org/W4293861233","https://openalex.org/W4296122973","https://openalex.org/W4312468136","https://openalex.org/W4312765357","https://openalex.org/W4313019229","https://openalex.org/W4379929801","https://openalex.org/W4386076092","https://openalex.org/W4386076358","https://openalex.org/W4389213248","https://openalex.org/W4390873172","https://openalex.org/W4390874575","https://openalex.org/W4402716098"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Multiple":[0],"Instance":[1],"Learning":[2],"(MIL)":[3],"represents":[4,102],"the":[5,63,115,136,186,200,206],"predominant":[6],"framework":[7],"in":[8,223],"Whole":[9],"Slide":[10],"Image":[11],"(WSI)":[12],"classification,":[13],"covering":[14],"aspects":[15],"such":[16,34],"as":[17,35],"sub-typing,":[18],"diagnosis,":[19],"and":[20,45,61,87,146,167,189,208],"beyond.":[21],"Current":[22],"MIL":[23,78],"models":[24,33,38],"predominantly":[25],"rely":[26],"on":[27,143,205],"instance-level":[28],"features":[29,47,113,173],"derived":[30],"from":[31,48,130],"pretrained":[32],"ResNet.":[36],"These":[37],"segment":[39],"each":[40],"WSI":[41],"into":[42],"independent":[43],"patches":[44],"extract":[46],"these":[49,169],"local":[50,68],"patches,":[51],"leading":[52],"to":[53,66,152,180,197],"a":[54,76,103,147,159],"significant":[55],"loss":[56],"of":[57,138,174,192],"global":[58],"spatial":[59,84,90,127,144,193],"context":[60,91,128,145,194],"restricting":[62],"model's":[64,201],"focus":[65],"merely":[67],"features.":[69],"To":[70],"address":[71],"this":[72],"issue,":[73],"we":[74],"propose":[75],"novel":[77],"framework,":[79],"named":[80],"SAM-MIL,":[81],"that":[82,109,214],"emphasizes":[83],"contextual":[85],"awareness":[86],"explicitly":[88],"incorporates":[89],"by":[92],"extracting":[93,126],"comprehensive,":[94],"image-level":[95],"information.":[96],"The":[97],"Segment":[98],"Anything":[99],"Model":[100],"(SAM)":[101],"pioneering":[104],"visual":[105],"segmentation":[106,112,165],"foundational":[107],"model":[108,218],"can":[110],"capture":[111],"without":[114],"need":[116],"for":[117,125,163],"additional":[118,182],"fine-tuning,":[119],"rendering":[120],"it":[121],"an":[122],"outstanding":[123],"tool":[124],"directly":[129],"raw":[131],"WSIs.":[132],"Our":[133,226],"approach":[134],"includes":[135],"design":[137],"group":[139,172],"feature":[140],"extraction":[141],"based":[142],"SAM-Guided":[148],"Group":[149],"Masking":[150],"strategy":[151],"mitigate":[153],"class":[154],"imbalance":[155],"issues.":[156],"We":[157],"implement":[158],"dynamic":[160],"mask":[161],"ratio":[162],"different":[164],"categories":[166],"supplement":[168],"with":[170],"representative":[171],"categories.":[175],"Moreover,":[176],"SAM-MIL":[177,217],"divides":[178],"instances":[179],"generate":[181],"pseudo-bags,":[183],"thereby":[184],"augmenting":[185],"training":[187],"set,":[188],"introduces":[190],"consistency":[191],"across":[195],"pseudo-bags":[196],"further":[198],"enhance":[199],"performance.":[202],"Experimental":[203],"results":[204],"CAMELYON-16":[207],"TCGA":[209],"Lung":[210],"Cancer":[211],"datasets":[212],"demonstrate":[213],"our":[215],"proposed":[216],"outperforms":[219],"existing":[220],"mainstream":[221],"methods":[222],"WSIs":[224],"classification.":[225],"open-source":[227],"implementation":[228],"code":[229],"is":[230,231],"available":[232],"at":[233],"https://github.com/FangHeng/SAM-MIL.":[234]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":6}],"updated_date":"2026-06-25T08:15:23.626066","created_date":"2025-10-10T00:00:00"}