{"id":"https://openalex.org/W2964242896","doi":"https://doi.org/10.1109/isbi.2018.8363704","title":"Adversarial deep structured nets for mass segmentation from mammograms","display_name":"Adversarial deep structured nets for mass segmentation from mammograms","publication_year":2018,"publication_date":"2018-04-01","ids":{"openalex":"https://openalex.org/W2964242896","doi":"https://doi.org/10.1109/isbi.2018.8363704","mag":"2964242896"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2018.8363704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2018.8363704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","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/A5031854562","display_name":"Wentao Zhu","orcid":"https://orcid.org/0000-0001-9290-1778"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wentao Zhu","raw_affiliation_strings":["University of California, Irvine"],"affiliations":[{"raw_affiliation_string":"University of California, Irvine","institution_ids":["https://openalex.org/I204250578"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060817282","display_name":"Xiang Xiang","orcid":"https://orcid.org/0000-0003-0606-6193"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang Xiang","raw_affiliation_strings":["Johns Hopkins University"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins University","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101413113","display_name":"Trac D. Tran","orcid":"https://orcid.org/0000-0002-0421-8416"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Trac D. Tran","raw_affiliation_strings":["Johns Hopkins University"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins University","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041383246","display_name":"Gregory D. Hager","orcid":"https://orcid.org/0000-0002-6662-9763"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gregory D. Hager","raw_affiliation_strings":["Johns Hopkins University"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins University","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084618257","display_name":"Xiaohui Xie","orcid":"https://orcid.org/0000-0002-5479-6345"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaohui Xie","raw_affiliation_strings":["University of California, Irvine"],"affiliations":[{"raw_affiliation_string":"University of California, Irvine","institution_ids":["https://openalex.org/I204250578"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5031854562"],"corresponding_institution_ids":["https://openalex.org/I204250578"],"apc_list":null,"apc_paid":null,"fwci":15.0623,"has_fulltext":false,"cited_by_count":140,"citation_normalized_percentile":{"value":0.99083412,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"847","last_page":"850"},"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":0.9965999722480774,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9939000010490417,"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/conditional-random-field","display_name":"Conditional random field","score":0.8952368497848511},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7134643793106079},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7092186212539673},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6991497278213501},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5767396092414856},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.558320164680481},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.49979162216186523},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48510968685150146},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4512939155101776},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4507442116737366},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4195692241191864},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33568763732910156}],"concepts":[{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.8952368497848511},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7134643793106079},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7092186212539673},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6991497278213501},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5767396092414856},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.558320164680481},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.49979162216186523},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48510968685150146},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4512939155101776},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4507442116737366},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4195692241191864},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33568763732910156},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","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":1,"locations":[{"id":"doi:10.1109/isbi.2018.8363704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2018.8363704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","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":20,"referenced_works":["https://openalex.org/W41027960","https://openalex.org/W304373761","https://openalex.org/W1500230509","https://openalex.org/W1546431092","https://openalex.org/W1654698919","https://openalex.org/W1673923490","https://openalex.org/W1903029394","https://openalex.org/W2116288467","https://openalex.org/W2116877738","https://openalex.org/W2124592697","https://openalex.org/W2131807858","https://openalex.org/W2161236525","https://openalex.org/W2294923432","https://openalex.org/W2524594445","https://openalex.org/W2738232076","https://openalex.org/W2952793010","https://openalex.org/W2964137552","https://openalex.org/W2964153729","https://openalex.org/W2964275459","https://openalex.org/W6637162671"],"related_works":["https://openalex.org/W2356597680","https://openalex.org/W2114846443","https://openalex.org/W3102147106","https://openalex.org/W2093471820","https://openalex.org/W2347460059","https://openalex.org/W50079190","https://openalex.org/W3136048405","https://openalex.org/W182104056","https://openalex.org/W3143595119","https://openalex.org/W2208878950"],"abstract_inverted_index":{"Mass":[0],"segmentation":[1,24,88],"provides":[2],"effective":[3],"morphological":[4],"features":[5],"which":[6,25],"are":[7],"important":[8],"for":[9,21],"mass":[10,23,50],"diagnosis.":[11],"In":[12],"this":[13],"work,":[14],"we":[15,66],"propose":[16],"a":[17,27,34,39,62],"novel":[18],"end-to-end":[19,103],"network":[20,30,104],"mammographic":[22],"employs":[26],"fully":[28],"convolutional":[29],"(FCN)":[31],"to":[32,44,70,74,85],"model":[33],"potential":[35],"function,":[36],"followed":[37],"by":[38],"conditional":[40],"random":[41],"field":[42],"(CRF)":[43],"perform":[45],"structured":[46],"learning.":[47],"Because":[48],"the":[49,57,75,87],"distribution":[51],"varies":[52],"greatly":[53],"with":[54,61],"pixel":[55],"position,":[56],"FCN":[58,82],"is":[59,83],"combined":[60],"position":[63],"priori.":[64],"Further,":[65],"employ":[67],"adversarial":[68],"training":[69],"eliminate":[71],"over-fitting":[72],"due":[73],"small":[76],"sizes":[77],"of":[78],"mammogram":[79],"datasets.":[80],"Multi-scale":[81],"employed":[84],"improve":[86],"performance.":[89],"Experimental":[90],"results":[91],"on":[92],"two":[93],"public":[94],"datasets,":[95],"IN":[96],"breast":[97],"and":[98],"DDSM-BCRP,":[99],"demonstrate":[100],"that":[101],"our":[102],"achieves":[105],"better":[106],"performance":[107],"than":[108],"state-of-the-art":[109],"approaches.":[110]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":26},{"year":2019,"cited_by_count":29},{"year":2018,"cited_by_count":12}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
