{"id":"https://openalex.org/W2912354537","doi":"https://doi.org/10.1109/access.2019.2898044","title":"Classification of Breast Cancer Histology Images Using Multi-Size and Discriminative Patches Based on Deep Learning","display_name":"Classification of Breast Cancer Histology Images Using Multi-Size and Discriminative Patches Based on Deep Learning","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2912354537","doi":"https://doi.org/10.1109/access.2019.2898044","mag":"2912354537"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2898044","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2898044","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08636921.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08636921.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100610224","display_name":"Yuqian Li","orcid":"https://orcid.org/0000-0002-6364-5063"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuqian Li","raw_affiliation_strings":["College of Computer Science and Technology, University of Science and Technology of China, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0002-6364-5063","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112446278","display_name":"Junmin Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I308837","display_name":"Suzhou University of Science and Technology","ror":"https://ror.org/04en8wb91","country_code":"CN","type":"education","lineage":["https://openalex.org/I308837"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junmin Wu","raw_affiliation_strings":["College of Software, University of Science and Technology of China, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Software, University of Science and Technology of China, Suzhou, China","institution_ids":["https://openalex.org/I126520041","https://openalex.org/I308837"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076758349","display_name":"Qisong Wu","orcid":"https://orcid.org/0000-0002-2114-7672"},"institutions":[{"id":"https://openalex.org/I4210094233","display_name":"Suzhou Kowloon Hospital","ror":"https://ror.org/00kkxne40","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210094233"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qisong Wu","raw_affiliation_strings":["Pathology Department, Suzhou Kowloon Hospital Institute, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pathology Department, Suzhou Kowloon Hospital Institute, Suzhou, China","institution_ids":["https://openalex.org/I4210094233"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":14.1021,"has_fulltext":true,"cited_by_count":155,"citation_normalized_percentile":{"value":0.99077307,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"7","issue":null,"first_page":"21400","last_page":"21408"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":1.0,"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":1.0,"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":0.9979000091552734,"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":0.9955000281333923,"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/discriminative-model","display_name":"Discriminative model","score":0.7853801250457764},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7139758467674255},{"id":"https://openalex.org/keywords/histology","display_name":"Histology","score":0.6926665306091309},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.641181468963623},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6038777828216553},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5672182440757751},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.5439767837524414},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.455170214176178},{"id":"https://openalex.org/keywords/h-e-stain","display_name":"H&E stain","score":0.43816953897476196},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4188670516014099},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.39787331223487854},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.27931082248687744},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.24271729588508606}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7853801250457764},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7139758467674255},{"id":"https://openalex.org/C57742111","wikidata":"https://www.wikidata.org/wiki/Q7168","display_name":"Histology","level":2,"score":0.6926665306091309},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.641181468963623},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6038777828216553},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5672182440757751},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.5439767837524414},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.455170214176178},{"id":"https://openalex.org/C125473707","wikidata":"https://www.wikidata.org/wiki/Q9914","display_name":"H&E stain","level":3,"score":0.43816953897476196},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4188670516014099},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.39787331223487854},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.27931082248687744},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.24271729588508606},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C74864618","wikidata":"https://www.wikidata.org/wiki/Q2332446","display_name":"Staining","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2898044","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2898044","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08636921.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:ec9b217338754c3bafc1acbf49936d96","is_oa":true,"landing_page_url":"https://doaj.org/article/ec9b217338754c3bafc1acbf49936d96","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 21400-21408 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2898044","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2898044","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08636921.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1870797585","display_name":null,"funder_award_id":"2016YF B1000403","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2912354537.pdf","grobid_xml":"https://content.openalex.org/works/W2912354537.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W1532736374","https://openalex.org/W1932847118","https://openalex.org/W1965451568","https://openalex.org/W2056499382","https://openalex.org/W2085721351","https://openalex.org/W2093030207","https://openalex.org/W2102605133","https://openalex.org/W2103243046","https://openalex.org/W2108598243","https://openalex.org/W2114535528","https://openalex.org/W2117539524","https://openalex.org/W2129112648","https://openalex.org/W2132162500","https://openalex.org/W2134993189","https://openalex.org/W2148309496","https://openalex.org/W2151608510","https://openalex.org/W2162931300","https://openalex.org/W2163605009","https://openalex.org/W2175543269","https://openalex.org/W2194775991","https://openalex.org/W2200374509","https://openalex.org/W2203051527","https://openalex.org/W2344480160","https://openalex.org/W2346062110","https://openalex.org/W2395611524","https://openalex.org/W2474421929","https://openalex.org/W2554892747","https://openalex.org/W2613181504","https://openalex.org/W2620578070","https://openalex.org/W2745940724","https://openalex.org/W2772723798","https://openalex.org/W2787746658","https://openalex.org/W2919115771","https://openalex.org/W2963395517","https://openalex.org/W3098150009","https://openalex.org/W3107885319","https://openalex.org/W4299518610","https://openalex.org/W6682132143","https://openalex.org/W6683965311","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W2956046890","https://openalex.org/W4312307642"],"abstract_inverted_index":{"The":[0,54,139,173],"diagnosis":[1,24],"of":[2,56,151,181],"breast":[3,57,152],"cancer":[4,58,153],"histology":[5,51,59,94,154],"images":[6,52,60,155],"with":[7,76],"hematoxylin":[8],"and":[9,15,33,64,73,80,89,98,132,156,165],"eosin":[10],"stained":[11],"is":[12,67,145],"non-trivial,":[13],"labor-intensive":[14],"often":[16],"leads":[17],"to":[18,27,69,134,147,178],"a":[19,123],"disagreement":[20],"between":[21],"pathologists.":[22],"Computer-assisted":[23],"systems":[25],"contribute":[26],"help":[28],"pathologists":[29],"improve":[30],"diagnostic":[31],"consistency":[32],"efficiency.":[34],"With":[35],"the":[36,117,129,148,161,169,179],"recent":[37],"advances":[38],"in":[39,142],"deep":[40],"learning,":[41],"convolutional":[42],"neural":[43],"networks":[44],"(CNNs)":[45],"have":[46],"been":[47],"successfully":[48],"used":[49],"for":[50],"analysis.":[53],"classification":[55,150],"into":[61],"normal,":[62],"benign,":[63],"malignant":[65],"sub-classes":[66],"related":[68],"cells'":[70],"density,":[71],"variability,":[72],"organization":[74],"along":[75],"overall":[77,170],"tissue":[78],"structure":[79],"morphology.":[81],"Based":[82],"on":[83,128,160,168],"this,":[84],"we":[85,121],"extract":[86],"both":[87],"smaller":[88],"larger":[90],"size":[91],"patches":[92,108],"from":[93],"images,":[95],"including":[96],"cell-level":[97,107],"tissue-level":[99],"features,":[100],"respectively.":[101],"However,":[102],"there":[103],"are":[104,175],"some":[105],"sampled":[106],"that":[109,115],"do":[110],"not":[111],"contain":[112],"enough":[113],"information":[114],"matches":[116],"image":[118],"tag.":[119],"Therefore,":[120],"propose":[122],"patches'":[124],"screening":[125],"method":[126],"based":[127],"clustering":[130],"algorithm":[131],"CNN":[133],"select":[135],"more":[136],"discriminative":[137],"patches.":[138],"approach":[140],"proposed":[141],"this":[143],"paper":[144],"applied":[146],"4-class":[149],"achieves":[157],"95%":[158],"accuracy":[159,167],"initial":[162],"test":[163,171],"set":[164],"88.89%":[166],"set.":[172],"results":[174,180],"competitive":[176],"compared":[177],"other":[182],"state-of-the-art":[183],"methods.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":25},{"year":2022,"cited_by_count":27},{"year":2021,"cited_by_count":41},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
