{"id":"https://openalex.org/W3011769840","doi":"https://doi.org/10.1117/12.2550636","title":"Graph convolutional networks for region of interest classification in breast histopathology","display_name":"Graph convolutional networks for region of interest classification in breast histopathology","publication_year":2020,"publication_date":"2020-03-16","ids":{"openalex":"https://openalex.org/W3011769840","doi":"https://doi.org/10.1117/12.2550636","mag":"3011769840"},"language":"en","primary_location":{"id":"doi:10.1117/12.2550636","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2550636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Digital Pathology","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/11511/88447","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073615924","display_name":"Bulut Ayg\u00fcne\u015f","orcid":null},"institutions":[{"id":"https://openalex.org/I168864056","display_name":"Bilkent University","ror":"https://ror.org/02vh8a032","country_code":"TR","type":"education","lineage":["https://openalex.org/I168864056"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Bulut Ayg\u00fcne\u015f","raw_affiliation_strings":["Bilkent Univ. (Turkey)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bilkent Univ. (Turkey)","institution_ids":["https://openalex.org/I168864056"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003826893","display_name":"Selim Aksoy","orcid":"https://orcid.org/0000-0003-4185-0565"},"institutions":[{"id":"https://openalex.org/I168864056","display_name":"Bilkent University","ror":"https://ror.org/02vh8a032","country_code":"TR","type":"education","lineage":["https://openalex.org/I168864056"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Selim Aksoy","raw_affiliation_strings":["Bilkent Univ. (Turkey)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bilkent Univ. (Turkey)","institution_ids":["https://openalex.org/I168864056"]}]},{"author_position":"middle","author":{"id":null,"display_name":"G\u00f6kberk Cinbi\u015f","orcid":null},"institutions":[{"id":"https://openalex.org/I201799495","display_name":"Middle East Technical University","ror":"https://ror.org/014weej12","country_code":"TR","type":"education","lineage":["https://openalex.org/I201799495"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"G\u00f6kberk Cinbi\u015f","raw_affiliation_strings":["Middle East Technical Univ. (Turkey)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Middle East Technical Univ. (Turkey)","institution_ids":["https://openalex.org/I201799495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041034673","display_name":"Kemal K\u00f6semehmeto\u011flu","orcid":"https://orcid.org/0000-0002-7747-0460"},"institutions":[{"id":"https://openalex.org/I66514158","display_name":"Hacettepe University","ror":"https://ror.org/04kwvgz42","country_code":"TR","type":"education","lineage":["https://openalex.org/I66514158"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Kemal K\u00f6semehmetoglu","raw_affiliation_strings":["Hacettepe Univ. (Turkey)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hacettepe Univ. (Turkey)","institution_ids":["https://openalex.org/I66514158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023172355","display_name":"Sevgen \u00d6nder","orcid":"https://orcid.org/0000-0002-5523-0669"},"institutions":[{"id":"https://openalex.org/I66514158","display_name":"Hacettepe University","ror":"https://ror.org/04kwvgz42","country_code":"TR","type":"education","lineage":["https://openalex.org/I66514158"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Sevgen \u00d6nder","raw_affiliation_strings":["Hacettepe Univ. (Turkey)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hacettepe Univ. (Turkey)","institution_ids":["https://openalex.org/I66514158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113525610","display_name":"Ay\u015feg\u00fcl \u00dcner","orcid":null},"institutions":[{"id":"https://openalex.org/I66514158","display_name":"Hacettepe University","ror":"https://ror.org/04kwvgz42","country_code":"TR","type":"education","lineage":["https://openalex.org/I66514158"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ay\u015feg\u00fcl \u00dcner","raw_affiliation_strings":["Hacettepe Univ. (Turkey)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hacettepe Univ. (Turkey)","institution_ids":["https://openalex.org/I66514158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.07,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.89605651,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"19","last_page":"19"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998000264167786,"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":0.9998000264167786,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9922999739646912,"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"}},{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9606999754905701,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7164201140403748},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6988149881362915},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.673682451248169},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6190418004989624},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5986708402633667},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.5196215510368347},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4973917305469513},{"id":"https://openalex.org/keywords/region-of-interest","display_name":"Region of interest","score":0.4765925109386444},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.45859140157699585},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.43968531489372253},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.24305874109268188},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2067771852016449},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.14261910319328308}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7164201140403748},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6988149881362915},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.673682451248169},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6190418004989624},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5986708402633667},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5196215510368347},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4973917305469513},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.4765925109386444},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.45859140157699585},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.43968531489372253},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.24305874109268188},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2067771852016449},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.14261910319328308},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1117/12.2550636","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2550636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Digital Pathology","raw_type":"proceedings-article"},{"id":"pmh:oai:https://open.metu.edu.tr:11511/88447","is_oa":true,"landing_page_url":"https://hdl.handle.net/11511/88447","pdf_url":null,"source":{"id":"https://openalex.org/S4306402495","display_name":"OpenMETU (Middle East Technical University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I201799495","host_organization_name":"Middle East Technical University","host_organization_lineage":["https://openalex.org/I201799495"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"},{"id":"pmh:oai:repository.bilkent.edu.tr:11693/77156","is_oa":false,"landing_page_url":"http://hdl.handle.net/11693/77156","pdf_url":null,"source":{"id":"https://openalex.org/S4306400079","display_name":"Bilkent University Institutional Repository (Bilkent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I168864056","host_organization_name":"Bilkent University","host_organization_lineage":["https://openalex.org/I168864056"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Progress in Biomedical Optics and Imaging","raw_type":"Conference Paper"}],"best_oa_location":{"id":"pmh:oai:https://open.metu.edu.tr:11511/88447","is_oa":true,"landing_page_url":"https://hdl.handle.net/11511/88447","pdf_url":null,"source":{"id":"https://openalex.org/S4306402495","display_name":"OpenMETU (Middle East Technical University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I201799495","host_organization_name":"Middle East Technical University","host_organization_lineage":["https://openalex.org/I201799495"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2964954556","https://openalex.org/W3019910406","https://openalex.org/W2952813363","https://openalex.org/W4378678253","https://openalex.org/W2911497689","https://openalex.org/W4360783045","https://openalex.org/W2770149305","https://openalex.org/W2972076240","https://openalex.org/W3167930666","https://openalex.org/W3014952856"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"approaches":[2,21,96,184],"have":[3,63],"shown":[4],"highly":[5],"successful":[6],"performance":[7],"in":[8,19,45,56,109,153],"the":[9,38,42,77,98,105,110,159,187,192],"categorization":[10,50],"of":[11,30,34,51,53,80,94,101],"digitized":[12],"biopsy":[13,57],"samples.":[14],"The":[15,68,142,166],"commonly":[16,203],"used":[17,204],"setting":[18],"these":[20,60,85,95],"is":[22,74,97],"to":[23,71,75,87,125,147],"employ":[24],"convolutional":[25,198],"neural":[26],"networks":[27],"for":[28,173],"classification":[29,78,90,177],"data":[31,171],"sets":[32],"consisting":[33],"images":[35,86],"all":[36],"having":[37],"same":[39],"size.":[40],"However,":[41],"clinical":[43],"practice":[44],"breast":[46],"histopathology":[47],"necessitates":[48],"multi-class":[49],"regions":[52,61],"interest":[54],"(ROI)":[55],"samples":[58],"where":[59,104],"can":[62],"arbitrary":[64],"shapes":[65],"and":[66,179],"sizes.":[67],"typical":[69],"solution":[70],"this":[72],"problem":[73],"aggregate":[76],"results":[79],"fixed-sized":[81],"patches":[82,103,152],"cropped":[83],"from":[84],"obtain":[88],"image-level":[89],"scores.":[91],"Another":[92],"limitation":[93],"independent":[99],"processing":[100],"individual":[102],"rich":[106],"contextual":[107],"information":[108,149],"complex":[111],"tissue":[112],"structures":[113],"has":[114],"not":[115],"yet":[116],"been":[117],"sufficiently":[118],"exploited.":[119],"We":[120],"propose":[121],"a":[122,131,138,154,163,169,174],"generic":[123],"methodology":[124],"incorporate":[126],"local":[127],"inter-patch":[128],"context":[129,194],"through":[130],"graph":[132,197],"convolution":[133],"network":[134],"(GCN)":[135],"that":[136,186,190],"admits":[137],"graph-based":[139],"ROI":[140,161],"representation.":[141],"proposed":[143,188],"GCN":[144],"model":[145,189],"aims":[146],"propagate":[148],"over":[150],"neighboring":[151],"progressive":[155],"manner":[156],"towards":[157],"classifying":[158],"whole":[160],"into":[162],"diagnostic":[164],"class.":[165],"experiments":[167],"using":[168,196],"challenging":[170],"set":[172],"4-class":[175],"ROI-level":[176],"task":[178],"comparisons":[180],"with":[181],"several":[182],"baseline":[183],"show":[185],"incorporates":[191],"spatial":[193],"by":[195],"layers":[199],"performs":[200],"better":[201],"than":[202],"fusion":[205],"rules.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
