{"id":"https://openalex.org/W2889934730","doi":"https://doi.org/10.1109/icip.2018.8451510","title":"Bi-Rads Classification of Breast Cancer: A New Pre-Processing Pipeline for Deep Models Training","display_name":"Bi-Rads Classification of Breast Cancer: A New Pre-Processing Pipeline for Deep Models Training","publication_year":2018,"publication_date":"2018-09-07","ids":{"openalex":"https://openalex.org/W2889934730","doi":"https://doi.org/10.1109/icip.2018.8451510","mag":"2889934730"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2018.8451510","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451510","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","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/A5101961906","display_name":"In\u00eas Domingues","orcid":"https://orcid.org/0000-0002-2334-7280"},"institutions":[{"id":"https://openalex.org/I76903346","display_name":"University of Coimbra","ror":"https://ror.org/04z8k9a98","country_code":"PT","type":"education","lineage":["https://openalex.org/I76903346"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Ines Domingues","raw_affiliation_strings":["Department of Informatics Engineering, University of Coimbra - CISUC, Coimbra, Portugal"],"affiliations":[{"raw_affiliation_string":"Department of Informatics Engineering, University of Coimbra - CISUC, Coimbra, Portugal","institution_ids":["https://openalex.org/I76903346"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065859612","display_name":"Pedro Henriques Abreu","orcid":"https://orcid.org/0000-0002-9278-8194"},"institutions":[{"id":"https://openalex.org/I76903346","display_name":"University of Coimbra","ror":"https://ror.org/04z8k9a98","country_code":"PT","type":"education","lineage":["https://openalex.org/I76903346"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Pedro H. Abreu","raw_affiliation_strings":["Department of Informatics Engineering, University of Coimbra - CISUC, Coimbra, Portugal"],"affiliations":[{"raw_affiliation_string":"Department of Informatics Engineering, University of Coimbra - CISUC, Coimbra, Portugal","institution_ids":["https://openalex.org/I76903346"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082299802","display_name":"Jo\u00e3o Santos","orcid":"https://orcid.org/0000-0003-2465-5143"},"institutions":[{"id":"https://openalex.org/I4210111164","display_name":"IPO Porto","ror":"https://ror.org/027ras364","country_code":"PT","type":"healthcare","lineage":["https://openalex.org/I4210111164"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Joao Santos","raw_affiliation_strings":["CI-IPOP, IPO-Porto Research Center, Porto, Portugal"],"affiliations":[{"raw_affiliation_string":"CI-IPOP, IPO-Porto Research Center, Porto, Portugal","institution_ids":["https://openalex.org/I4210111164"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101961906"],"corresponding_institution_ids":["https://openalex.org/I76903346"],"apc_list":null,"apc_paid":null,"fwci":2.0447,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.90069097,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1378","last_page":"1382"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9994999766349792,"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":0.9994999766349792,"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":0.984000027179718,"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":0.9513000249862671,"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":0.8566091060638428},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6938488483428955},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6513667106628418},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6283873319625854},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5577118992805481},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5465114712715149},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43494945764541626},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43146300315856934},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4135904908180237}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8566091060638428},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6938488483428955},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6513667106628418},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6283873319625854},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5577118992805481},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5465114712715149},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43494945764541626},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43146300315856934},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4135904908180237},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2018.8451510","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451510","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W304373761","https://openalex.org/W639708223","https://openalex.org/W1008614918","https://openalex.org/W1686810756","https://openalex.org/W2064129574","https://openalex.org/W2121651901","https://openalex.org/W2151103935","https://openalex.org/W2163605009","https://openalex.org/W2216351247","https://openalex.org/W2473985421","https://openalex.org/W2493683088","https://openalex.org/W2527654160","https://openalex.org/W2605960745","https://openalex.org/W2734847897","https://openalex.org/W2750023899","https://openalex.org/W2751658105","https://openalex.org/W2761168727","https://openalex.org/W2962835968","https://openalex.org/W2963173190","https://openalex.org/W2964189045","https://openalex.org/W2964275459","https://openalex.org/W6620707391","https://openalex.org/W6637373629","https://openalex.org/W6648737282","https://openalex.org/W6666340103","https://openalex.org/W6684191040","https://openalex.org/W6720841421","https://openalex.org/W6741593600","https://openalex.org/W6743791534"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W2981877337","https://openalex.org/W3203938600"],"abstract_inverted_index":{"One":[0],"of":[1,8,74,91,126],"the":[2,6,16,45,86,89,95,105,120,124,129,135,141,147],"main":[3],"difficulties":[4],"in":[5,12,34,81,88],"use":[7],"deep":[9,57],"learning":[10],"strategies":[11],"medical":[13,35],"contexts":[14,36],"is":[15,66,76],"training":[17,26],"set":[18],"size.":[19],"While":[20],"these":[21,28],"methods":[22],"need":[23],"large":[24],"annotated":[25],"sets,":[27],"datasets":[29],"are":[30,52,79],"costly":[31],"to":[32,54],"obtain":[33],"and":[37,41,128,139],"suffer":[38],"from":[39],"intra":[40],"inter-subject":[42],"variability.":[43],"In":[44],"present":[46],"work,":[47],"two":[48],"new":[49],"pre-processing":[50],"techniques":[51],"introduced":[53],"improve":[55],"a":[56,82,99,111],"classifier":[58],"performance.":[59],"First,":[60],"data":[61,130],"augmentation":[62,131],"based":[63,71],"on":[64,72],"co-registration":[65],"suggested.":[67],"Then,":[68],"multi-scale":[69],"enhancement":[70],"Difference":[73,125],"Gaussians":[75,127],"proposed.":[77],"Results":[78],"accessed":[80],"public":[83],"mammogram":[84],"database,":[85],"InBreast,":[87],"context":[90],"an":[92],"ordinal":[93],"problem,":[94],"BI-RADS":[96],"classification.":[97],"Moreover,":[98],"pre-trained":[100],"Convolutional":[101],"Neural":[102],"Network":[103],"with":[104,123],"AlexNet":[106],"architecture":[107],"was":[108],"used":[109],"as":[110],"base":[112],"classifier.":[113],"The":[114],"multi-class":[115],"classification":[116],"experiments":[117],"show":[118],"that":[119],"proposed":[121],"pipeline":[122],"technique":[132],"outperforms":[133],"using":[134,140],"original":[136,142],"dataset":[137,143],"only":[138],"augmented":[144],"by":[145],"mirroring":[146],"images.":[148]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
