{"id":"https://openalex.org/W2200566924","doi":"https://doi.org/10.1109/bibm.2015.7359868","title":"Probabilistic visual search for masses within mammography images using deep learning","display_name":"Probabilistic visual search for masses within mammography images using deep learning","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2200566924","doi":"https://doi.org/10.1109/bibm.2015.7359868","mag":"2200566924"},"language":"en","primary_location":{"id":"doi:10.1109/bibm.2015.7359868","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2015.7359868","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5023824008","display_name":"M. G\u00fcnhan Ertosun","orcid":null},"institutions":[{"id":"https://openalex.org/I4210137306","display_name":"Stanford Medicine","ror":"https://ror.org/03mtd9a03","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210137306","https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mehmet Gunhan Ertosun","raw_affiliation_strings":["Department of Radiology Stanford, School of Medicine, Stanford, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology Stanford, School of Medicine, Stanford, CA, USA","institution_ids":["https://openalex.org/I4210137306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004965117","display_name":"Daniel L. Rubin","orcid":"https://orcid.org/0000-0001-5057-4369"},"institutions":[{"id":"https://openalex.org/I4210137306","display_name":"Stanford Medicine","ror":"https://ror.org/03mtd9a03","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210137306","https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel L. Rubin","raw_affiliation_strings":["Departments of Radiology and Medicine, (Biomedical Informatics) Stanford School of Medicine, Stanford, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Departments of Radiology and Medicine, (Biomedical Informatics) Stanford School of Medicine, Stanford, CA, USA","institution_ids":["https://openalex.org/I4210137306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210137306"],"apc_list":null,"apc_paid":null,"fwci":4.321,"has_fulltext":false,"cited_by_count":115,"citation_normalized_percentile":{"value":0.95578603,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1310","last_page":"1315"},"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/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9940999746322632,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8382073640823364},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7605737447738647},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.701124906539917},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.5953942537307739},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5794157385826111},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5430561304092407},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5100386738777161},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.48384085297584534},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.45936980843544006},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4481314420700073},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.42132580280303955},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.41108986735343933},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3031861186027527}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8382073640823364},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7605737447738647},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.701124906539917},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.5953942537307739},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5794157385826111},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5430561304092407},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5100386738777161},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.48384085297584534},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.45936980843544006},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4481314420700073},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.42132580280303955},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.41108986735343933},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3031861186027527},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"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/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm.2015.7359868","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2015.7359868","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":26,"referenced_works":["https://openalex.org/W177004468","https://openalex.org/W1523493493","https://openalex.org/W1580459528","https://openalex.org/W1665214252","https://openalex.org/W1686810756","https://openalex.org/W1980527762","https://openalex.org/W1983190542","https://openalex.org/W1986916340","https://openalex.org/W2026395347","https://openalex.org/W2028177593","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2117498349","https://openalex.org/W2148516878","https://openalex.org/W2149603624","https://openalex.org/W2155893237","https://openalex.org/W2163605009","https://openalex.org/W2468567150","https://openalex.org/W2950094539","https://openalex.org/W6607184829","https://openalex.org/W6631319189","https://openalex.org/W6637242042","https://openalex.org/W6637373629","https://openalex.org/W6674914833","https://openalex.org/W6682139142","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2742991909","https://openalex.org/W2563096758","https://openalex.org/W2972035100","https://openalex.org/W2940977206","https://openalex.org/W2738461075","https://openalex.org/W2114586818","https://openalex.org/W3005023910","https://openalex.org/W2547232919","https://openalex.org/W3156786002","https://openalex.org/W2135447498"],"abstract_inverted_index":{"We":[0,70],"developed":[1],"a":[2,28,32,41,47,60,66,82],"deep":[3,48,67,142],"learning-based":[4],"visual":[5],"search":[6,13],"system":[7,23,104],"for":[8,74,123],"the":[9,54,57,75,92,106,121,135],"task":[10,76],"of":[11,16,25,77,91,97,108],"automated":[12],"and":[14,31,51,84,137,154],"localization":[15,33],"masses":[17,93],"in":[18],"whole":[19],"mammography":[20,116],"images.":[21],"The":[22],"consists":[24],"two":[26],"modules:":[27],"classification":[29],"engine":[30],"engine.":[34],"It":[35],"first":[36],"classifies":[37],"mammograms":[38],"as":[39,118,131],"containing":[40],"mass":[42,45],"or":[43,126,133],"no":[44],"using":[46,59],"learning":[49,68,143],"classifier,":[50],"then":[52],"localizes":[53],"mass(es)":[55],"within":[56],"image":[58,117,124,156],"regional":[61],"probabilistic":[62],"approach":[63],"based":[64,140],"on":[65,141],"network.":[69],"obtained":[71],"85%":[72,90],"accuracy":[73],"identifying":[78],"images":[79],"that":[80],"contain":[81],"mass,":[83],"we":[85],"were":[86],"able":[87,110],"to":[88,111],"localize":[89],"at":[94],"an":[95,114],"average":[96],"0.9":[98],"false":[99],"positives":[100],"per":[101],"image.":[102],"Our":[103],"has":[105],"advantages":[107],"being":[109],"work":[112],"with":[113,144],"entire":[115],"input":[119],"without":[120],"need":[122],"segmentation":[125],"other":[127],"pre-processing":[128],"steps,":[129],"such":[130],"cropping":[132],"tiling":[134],"image,":[136],"it":[138,149],"is":[139],"unsupervised":[145],"feature":[146],"discovery,":[147],"so":[148],"does":[150],"not":[151],"require":[152],"pre-defined":[153],"hand-crafted":[155],"features.":[157]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":18},{"year":2018,"cited_by_count":12},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
