{"id":"https://openalex.org/W2063395656","doi":"https://doi.org/10.1117/12.2082343","title":"Adaptive whole slide tissue segmentation to handle inter-slide tissue variability","display_name":"Adaptive whole slide tissue segmentation to handle inter-slide tissue variability","publication_year":2015,"publication_date":"2015-03-19","ids":{"openalex":"https://openalex.org/W2063395656","doi":"https://doi.org/10.1117/12.2082343","mag":"2063395656"},"language":"en","primary_location":{"id":"doi:10.1117/12.2082343","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2082343","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","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/A5038839161","display_name":"Kien Nguyen","orcid":"https://orcid.org/0000-0002-3466-9218"},"institutions":[{"id":"https://openalex.org/I4210133212","display_name":"Ventana Research Corporation (United States)","ror":"https://ror.org/039tznx53","country_code":"US","type":"company","lineage":["https://openalex.org/I4210133212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kien Nguyen","raw_affiliation_strings":["Ventana Medical Systems, Inc. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ventana Medical Systems, Inc. (United States)","institution_ids":["https://openalex.org/I4210133212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100443189","display_name":"Ting Chen","orcid":"https://orcid.org/0000-0002-3228-9166"},"institutions":[{"id":"https://openalex.org/I4210133212","display_name":"Ventana Research Corporation (United States)","ror":"https://ror.org/039tznx53","country_code":"US","type":"company","lineage":["https://openalex.org/I4210133212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ting Chen","raw_affiliation_strings":["Ventana Medical Systems, Inc. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ventana Medical Systems, Inc. (United States)","institution_ids":["https://openalex.org/I4210133212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018623004","display_name":"Joerg Bredno","orcid":"https://orcid.org/0000-0002-2302-2339"},"institutions":[{"id":"https://openalex.org/I4210133212","display_name":"Ventana Research Corporation (United States)","ror":"https://ror.org/039tznx53","country_code":"US","type":"company","lineage":["https://openalex.org/I4210133212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joerg Bredno","raw_affiliation_strings":["Ventana Medical Systems, Inc. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ventana Medical Systems, Inc. (United States)","institution_ids":["https://openalex.org/I4210133212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013484426","display_name":"Chukka Srinivas","orcid":null},"institutions":[{"id":"https://openalex.org/I4210133212","display_name":"Ventana Research Corporation (United States)","ror":"https://ror.org/039tznx53","country_code":"US","type":"company","lineage":["https://openalex.org/I4210133212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chukka Srinivas","raw_affiliation_strings":["Ventana Medical Systems, Inc. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ventana Medical Systems, Inc. (United States)","institution_ids":["https://openalex.org/I4210133212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079353895","display_name":"Christophe Chefd\u2019hotel","orcid":null},"institutions":[{"id":"https://openalex.org/I4210133212","display_name":"Ventana Research Corporation (United States)","ror":"https://ror.org/039tznx53","country_code":"US","type":"company","lineage":["https://openalex.org/I4210133212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christophe Chefd'hotel","raw_affiliation_strings":["Ventana Medical Systems, Inc. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ventana Medical Systems, Inc. (United States)","institution_ids":["https://openalex.org/I4210133212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019434935","display_name":"Solange Romagnoli","orcid":"https://orcid.org/0000-0001-5465-661X"},"institutions":[{"id":"https://openalex.org/I4210094361","display_name":"Roche Pharma AG (Germany)","ror":"https://ror.org/00sh68184","country_code":"DE","type":"company","lineage":["https://openalex.org/I118019719","https://openalex.org/I4210094361"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Solange Romagnoli","raw_affiliation_strings":["Roche Diagnostics GmbH (Germany)","Roche Diagnostics GmbH, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Roche Diagnostics GmbH (Germany)","institution_ids":["https://openalex.org/I4210094361"]},{"raw_affiliation_string":"Roche Diagnostics GmbH, Germany","institution_ids":["https://openalex.org/I4210094361"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028093485","display_name":"Astrid Heller","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094361","display_name":"Roche Pharma AG (Germany)","ror":"https://ror.org/00sh68184","country_code":"DE","type":"company","lineage":["https://openalex.org/I118019719","https://openalex.org/I4210094361"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Astrid Heller","raw_affiliation_strings":["Roche Diagnostics GmbH (Germany)","Roche Diagnostics GmbH, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Roche Diagnostics GmbH (Germany)","institution_ids":["https://openalex.org/I4210094361"]},{"raw_affiliation_string":"Roche Diagnostics GmbH, Germany","institution_ids":["https://openalex.org/I4210094361"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076641028","display_name":"Oliver Grimm","orcid":"https://orcid.org/0009-0002-0482-1298"},"institutions":[{"id":"https://openalex.org/I4210094361","display_name":"Roche Pharma AG (Germany)","ror":"https://ror.org/00sh68184","country_code":"DE","type":"company","lineage":["https://openalex.org/I118019719","https://openalex.org/I4210094361"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Oliver Grimm","raw_affiliation_strings":["Roche Diagnostics GmbH (Germany)","Roche Diagnostics GmbH, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Roche Diagnostics GmbH (Germany)","institution_ids":["https://openalex.org/I4210094361"]},{"raw_affiliation_string":"Roche Diagnostics GmbH, Germany","institution_ids":["https://openalex.org/I4210094361"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026646522","display_name":"Fabien Gaire","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094361","display_name":"Roche Pharma AG (Germany)","ror":"https://ror.org/00sh68184","country_code":"DE","type":"company","lineage":["https://openalex.org/I118019719","https://openalex.org/I4210094361"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Fabien Gaire","raw_affiliation_strings":["Roche Diagnostics GmbH (Germany)","Roche Diagnostics GmbH, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Roche Diagnostics GmbH (Germany)","institution_ids":["https://openalex.org/I4210094361"]},{"raw_affiliation_string":"Roche Diagnostics GmbH, Germany","institution_ids":["https://openalex.org/I4210094361"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04874306,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9420","issue":null,"first_page":"94200P","last_page":"94200P"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"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":0.9998999834060669,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9965999722480774,"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":0.9962000250816345,"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":0.7378390431404114},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.684580385684967},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6475716829299927},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5858457088470459},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5746423602104187},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5353518128395081},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5310222506523132},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5247887969017029},{"id":"https://openalex.org/keywords/standard-test-image","display_name":"Standard test image","score":0.4686550199985504},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44920387864112854},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43741628527641296},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42340540885925293},{"id":"https://openalex.org/keywords/digital-image","display_name":"Digital image","score":0.4113050699234009},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3068578243255615}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7378390431404114},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.684580385684967},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6475716829299927},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5858457088470459},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5746423602104187},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5353518128395081},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5310222506523132},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5247887969017029},{"id":"https://openalex.org/C180462255","wikidata":"https://www.wikidata.org/wiki/Q3559736","display_name":"Standard test image","level":4,"score":0.4686550199985504},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44920387864112854},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43741628527641296},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42340540885925293},{"id":"https://openalex.org/C42781572","wikidata":"https://www.wikidata.org/wiki/Q1250322","display_name":"Digital image","level":4,"score":0.4113050699234009},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3068578243255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2082343","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2082343","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","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":15,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1975672287","https://openalex.org/W1980359033","https://openalex.org/W2013500739","https://openalex.org/W2030041075","https://openalex.org/W2053811958","https://openalex.org/W2094099197","https://openalex.org/W2115891208","https://openalex.org/W2141967861","https://openalex.org/W2154053567","https://openalex.org/W2341168461","https://openalex.org/W6644134758","https://openalex.org/W6654026772","https://openalex.org/W6663701227","https://openalex.org/W6677337855"],"related_works":["https://openalex.org/W2952657063","https://openalex.org/W2130769947","https://openalex.org/W3034301390","https://openalex.org/W2267963390","https://openalex.org/W2165442833","https://openalex.org/W2265298723","https://openalex.org/W2090516435","https://openalex.org/W2980745533","https://openalex.org/W1597565744","https://openalex.org/W4381329051"],"abstract_inverted_index":{"Automatic":[0],"whole":[1],"slide":[2,93],"(WS)":[3],"tissue":[4,67,83,90],"image":[5,53,56,64,78,177,241],"segmentation":[6],"is":[7,27,112,231],"an":[8,219],"important":[9],"problem":[10,26],"in":[11,60,76,138,174,180],"digital":[12],"pathology.":[13],"A":[14],"conventional":[15],"classification-based":[16],"method":[17,70,164,230,277],"(referred":[18],"to":[19,23,28,50,94,97,130,203,217,223,264,270,278],"as":[20],"CCb":[21,133,182,293],"method)":[22,183],"tackle":[24],"this":[25,98,159],"train":[29],"a":[30,33,41,73,123,166,175,280],"classifier":[31],"on":[32],"pre-built":[34,150,209,215],"training":[35,44,109,221],"database":[36],"(pre-built":[37],"DB)":[38,210],"obtained":[39],"from":[40,72,92,107,148,158,207],"set":[42],"of":[43,89,115,237,245,283],"WS":[45,63,77,141,176,240,267,285],"images,":[46,286],"and":[47,184,251,258],"use":[48],"it":[49],"classify":[51],"all":[52,171],"pixels":[54],"or":[55],"patches":[57],"(test":[58],"samples)":[59],"the":[61,80,87,100,108,113,119,132,139,149,162,181,191,208,214,225,234,238,252,256,265,271,274,289,292],"test":[62,101,140,172,239],"into":[65],"different":[66,106],"types.":[68],"This":[69],"suffers":[71],"major":[74],"challenge":[75],"analysis:":[79],"strong":[81],"inter-slide":[82],"variability":[84,88,254],"(ISTV),":[85],"i.e.,":[86],"appearance":[91],"slide.":[95],"Due":[96],"ISTV,":[99,120],"samples":[102,173,192,248,261],"are":[103,153,211,249],"usually":[104],"very":[105],"data,":[110],"which":[111],"source":[114],"misclassification.":[116],"To":[117],"address":[118],"we":[121,287],"propose":[122],"novel":[124],"method,":[125],"called":[126],"slide-adapted":[127],"classification":[128,187],"(SAC),":[129],"extend":[131],"method.":[134,294],"We":[135],"assume":[136],"that":[137],"image,":[142],"besides":[143],"regions":[144,154],"with":[145,155,194,213],"high":[146,195,246,259],"variation":[147,157,206],"DB,":[151],"there":[152],"lower":[156,253],"DB.":[160],"Hence,":[161],"SAC":[163,276],"performs":[165],"two-stage":[167],"classification:":[168],"first":[169],"classifies":[170],"(as":[178],"done":[179],"compute":[185],"their":[186,204],"confidence":[188,196,227,247,260],"scores.":[189],"Next,":[190],"classified":[193,201],"scores":[197],"(samples":[198],"being":[199],"reliably":[200],"due":[202],"low":[205,226,257],"combined":[212],"DB":[216,222],"generate":[218],"adaptive":[220],"reclassify":[224],"samples.":[228],"The":[229],"motivated":[232],"by":[233],"large":[235,243,281],"size":[236],"(a":[242],"number":[244],"obtained),":[250],"between":[255],"(both":[262],"belonging":[263],"same":[266],"image)":[268],"compared":[269],"ISTV.":[272],"Using":[273],"proposed":[275],"segment":[279],"dataset":[282],"24":[284],"improve":[288],"accuracy":[290],"over":[291]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}