{"id":"https://openalex.org/W2028566684","doi":"https://doi.org/10.1117/12.2081793","title":"Detection of high-grade atypia nuclei in breast cancer imaging","display_name":"Detection of high-grade atypia nuclei in breast cancer imaging","publication_year":2015,"publication_date":"2015-03-17","ids":{"openalex":"https://openalex.org/W2028566684","doi":"https://doi.org/10.1117/12.2081793","mag":"2028566684"},"language":"en","primary_location":{"id":"doi:10.1117/12.2081793","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2081793","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/A5068194570","display_name":"Henri No\u00ebl","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094239","display_name":"Image and Pervasive Access Laboratory","ror":"https://ror.org/00m3mb357","country_code":"SG","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I4210094239"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Henri No\u00ebl","raw_affiliation_strings":["Image and Pervasive Access Lab. (Singapore)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Image and Pervasive Access Lab. (Singapore)","institution_ids":["https://openalex.org/I4210094239"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106418676","display_name":"Ludovic Roux","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094239","display_name":"Image and Pervasive Access Laboratory","ror":"https://ror.org/00m3mb357","country_code":"SG","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I4210094239"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Ludovic Roux","raw_affiliation_strings":["Image and Pervasive Access Lab. (Singapore)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Image and Pervasive Access Lab. (Singapore)","institution_ids":["https://openalex.org/I4210094239"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023507910","display_name":"Shijian Lu","orcid":"https://orcid.org/0000-0002-6766-2506"},"institutions":[{"id":"https://openalex.org/I4210094239","display_name":"Image and Pervasive Access Laboratory","ror":"https://ror.org/00m3mb357","country_code":"SG","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I4210094239"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Shijian Lu","raw_affiliation_strings":["Image and Pervasive Access Lab. (Singapore)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Image and Pervasive Access Lab. (Singapore)","institution_ids":["https://openalex.org/I4210094239"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029503346","display_name":"Thomas Boudier","orcid":"https://orcid.org/0000-0002-0148-7733"},"institutions":[{"id":"https://openalex.org/I4210094239","display_name":"Image and Pervasive Access Laboratory","ror":"https://ror.org/00m3mb357","country_code":"SG","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I4210094239"]},{"id":"https://openalex.org/I4210148498","display_name":"Bioinformatics Institute","ror":"https://ror.org/044w3nw43","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I2801752549","https://openalex.org/I4210148498"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Thomas Boudier","raw_affiliation_strings":["BioInformatics Institute (Singapore)","Image and Pervasive Access Lab. (Singapore)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BioInformatics Institute (Singapore)","institution_ids":["https://openalex.org/I4210148498"]},{"raw_affiliation_string":"Image and Pervasive Access Lab. (Singapore)","institution_ids":["https://openalex.org/I4210094239"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4452,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.76752998,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"9420","issue":null,"first_page":"94200R","last_page":"94200R"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9975000023841858,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9934999942779541,"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":0.7131940722465515},{"id":"https://openalex.org/keywords/nuclear-atypia","display_name":"Nuclear atypia","score":0.6777669191360474},{"id":"https://openalex.org/keywords/grading","display_name":"Grading (engineering)","score":0.6191587448120117},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6080830097198486},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5806662440299988},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.506513237953186},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.454770565032959},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.42806434631347656},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.32731062173843384},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.21924462914466858},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.18321681022644043},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.12776237726211548},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.08742761611938477}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7131940722465515},{"id":"https://openalex.org/C2781289014","wikidata":"https://www.wikidata.org/wiki/Q7068032","display_name":"Nuclear atypia","level":3,"score":0.6777669191360474},{"id":"https://openalex.org/C2777286243","wikidata":"https://www.wikidata.org/wiki/Q5591926","display_name":"Grading (engineering)","level":2,"score":0.6191587448120117},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6080830097198486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5806662440299988},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.506513237953186},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.454770565032959},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.42806434631347656},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.32731062173843384},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.21924462914466858},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.18321681022644043},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.12776237726211548},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.08742761611938477},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"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/C204232928","wikidata":"https://www.wikidata.org/wiki/Q899285","display_name":"Immunohistochemistry","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2081793","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2081793","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":[{"display_name":"Good health and well-being","score":0.75,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W22040386","https://openalex.org/W1981989535","https://openalex.org/W2010871781","https://openalex.org/W2112796928","https://openalex.org/W2132031490","https://openalex.org/W2138763931","https://openalex.org/W2143093135","https://openalex.org/W2916161913","https://openalex.org/W2963623381","https://openalex.org/W4205098750","https://openalex.org/W4239945174","https://openalex.org/W4285719527","https://openalex.org/W6759337328"],"related_works":["https://openalex.org/W2064165679","https://openalex.org/W1588461101","https://openalex.org/W3208525924","https://openalex.org/W4293226380","https://openalex.org/W2885058781","https://openalex.org/W4285411112","https://openalex.org/W2085033728","https://openalex.org/W1552490082","https://openalex.org/W2189583758","https://openalex.org/W2063738833"],"abstract_inverted_index":{"Along":[0],"with":[1],"mitotic":[2],"count,":[3],"nuclear":[4,7,45],"pleomorphism":[5],"or":[6],"atypia":[8,46],"is":[9],"an":[10,130],"important":[11],"criterion":[12],"for":[13,67,83,122],"the":[14,49,61,68,77,148,155,167],"grading":[15],"of":[16,53,55,63,71,80,111,115,147],"breast":[17,81],"cancer":[18,82],"in":[19,27,129,154],"histopathology.":[20],"Though":[21],"some":[22],"works":[23],"have":[24],"been":[25,41],"done":[26],"mitosis":[28,106],"detection":[29,54,70],"(ICPR":[30],"2012,<sup>1</sup>":[31],"MICCAI":[32],"2013,<sup>2</sup>":[33],"and":[34,107,170],"ICPR":[35,90],"2014),":[36],"not":[37,153],"much":[38],"work":[39],"has":[40],"dedicated":[42],"to":[43,97,174],"automated":[44,69],"grading,":[47],"especially":[48],"most":[50],"difficult":[51],"task":[52],"grade":[56],"3":[57],"nuclei.":[58],"We":[59,141,164],"propose":[60],"use":[62],"Convolutional":[64],"Neural":[65],"Networks":[66],"cell":[72],"nuclei,":[73,104],"using":[74],"images":[75,86,114,146],"from":[76,89],"three":[78,149],"grades":[79,150,159],"training.":[84,123],"The":[85],"were":[87,120,152,161,187],"obtained":[88],"contests.":[91],"Additional":[92],"manual":[93],"annotation":[94],"was":[95],"performed":[96,171],"classify":[98],"pixels":[99,119],"into":[100],"five":[101],"classes:":[102],"stroma,":[103],"lymphocytes,":[105],"fat.":[108],"At":[109],"total":[110],"3,000":[112],"thumbnail":[113],"101":[116,118],"&times;":[117],"used":[121],"By":[124],"dividing":[125],"this":[126],"training":[127,137,156],"set":[128],"80/20":[131],"ratio":[132],"we":[133],"could":[134],"obtain":[135],"good":[136],"results":[138,180],"(around":[139],"90%).":[140],"tested":[142],"our":[143],"CNN":[144],"on":[145],"which":[151],"set.":[157],"High":[158],"nuclei":[160,186],"correctly":[162,188],"classified.":[163],"then":[165],"thresholded":[166],"classification":[168],"map":[169],"basic":[172],"analysis":[173],"keep":[175],"only":[176],"rounded":[177],"objects.":[178],"Our":[179],"show":[181],"that":[182],"mostly":[183],"all":[184],"atypical":[185],"detected.":[189]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}