{"id":"https://openalex.org/W2790463788","doi":"https://doi.org/10.1117/12.2293976","title":"Saliency U-Net: A regional saliency map-driven hybrid deep learning network for anomaly segmentation","display_name":"Saliency U-Net: A regional saliency map-driven hybrid deep learning network for anomaly segmentation","publication_year":2018,"publication_date":"2018-02-27","ids":{"openalex":"https://openalex.org/W2790463788","doi":"https://doi.org/10.1117/12.2293976","mag":"2790463788"},"language":"en","primary_location":{"id":"doi:10.1117/12.2293976","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293976","pdf_url":null,"source":{"id":"https://openalex.org/S4306519508","display_name":"Medical Imaging 2018: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Computer-Aided Diagnosis","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/A5057284370","display_name":"Tanveer Syeda-Mahmood","orcid":"https://orcid.org/0000-0003-0059-3208"},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tanveer F. Syeda-Mahmood","raw_affiliation_strings":["IBM Research - Almaden (United States)"],"affiliations":[{"raw_affiliation_string":"IBM Research - Almaden (United States)","institution_ids":["https://openalex.org/I4210085935"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041913317","display_name":"Alexandros Karargyris","orcid":"https://orcid.org/0000-0002-1930-3410"},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexandros Karargyris","raw_affiliation_strings":["IBM Research Almaden, United States"],"affiliations":[{"raw_affiliation_string":"IBM Research Almaden, United States","institution_ids":["https://openalex.org/I4210085935"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5057284370"],"corresponding_institution_ids":["https://openalex.org/I4210085935"],"apc_list":null,"apc_paid":null,"fwci":0.5448,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.6283508,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"40","issue":null,"first_page":"64","last_page":"64"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9968000054359436,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9958999752998352,"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":0.7508773803710938},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7349442839622498},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6650316715240479},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6349797248840332},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4648531675338745},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34225353598594666}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7508773803710938},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7349442839622498},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6650316715240479},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6349797248840332},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4648531675338745},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34225353598594666}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2293976","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293976","pdf_url":null,"source":{"id":"https://openalex.org/S4306519508","display_name":"Medical Imaging 2018: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1537143564","https://openalex.org/W1826469673","https://openalex.org/W1901129140","https://openalex.org/W1910109443","https://openalex.org/W1986833874","https://openalex.org/W1991964403","https://openalex.org/W2083587970","https://openalex.org/W2131067764","https://openalex.org/W2401154299","https://openalex.org/W2468025758","https://openalex.org/W2545597282","https://openalex.org/W6632084685","https://openalex.org/W6638511236","https://openalex.org/W6639824700","https://openalex.org/W6646985408","https://openalex.org/W6679896483","https://openalex.org/W6713232366","https://openalex.org/W6729097466"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W2755342338","https://openalex.org/W2058170566","https://openalex.org/W2036807459","https://openalex.org/W2775347418","https://openalex.org/W1969923398","https://openalex.org/W2772917594","https://openalex.org/W2166024367","https://openalex.org/W3009238340"],"abstract_inverted_index":{"Deep":[0],"learning":[1,30,78,105],"networks":[2],"are":[3],"gaining":[4],"popularity":[5],"in":[6,45,127,140,147],"many":[7],"medical":[8],"image":[9,85],"analysis":[10],"tasks":[11],"due":[12],"to":[13,17,90,118],"their":[14],"generalized":[15],"ability":[16],"automatically":[18],"extract":[19],"relevant":[20],"features":[21],"from":[22],"raw":[23,84,112],"images.":[24],"However,":[25],"this":[26,70],"can":[27],"make":[28],"the":[29,54,57,94,111,128],"problem":[31],"unnecessarily":[32],"harder":[33],"requiring":[34],"network":[35,79,98,106],"architectures":[36],"of":[37,42,83,133],"high":[38],"complexity.":[39],"In":[40,69],"case":[41],"anomaly":[43,55],"detection,":[44],"particular,":[46],"there":[47],"is":[48],"often":[49],"sufficient":[50],"regional":[51,88],"difference":[52],"between":[53],"and":[56,86,113,123,137,144],"surrounding":[58],"parenchyma":[59],"that":[60],"could":[61],"be":[62],"easily":[63],"highlighted":[64],"through":[65],"bottom-up":[66],"saliency":[67],"operators.":[68],"paper":[71],"we":[72,101],"propose":[73],"a":[74,81,103],"new":[75],"hybrid":[76],"deep":[77,104],"using":[80,96,109,151],"combination":[82],"such":[87,152],"maps":[89],"more":[91],"accurately":[92],"learn":[93],"anomalies":[95],"simpler":[97],"architectures.":[99],"Specifically,":[100],"modify":[102],"called":[107],"U-Net":[108],"both":[110],"pre-segmented":[114],"images":[115,150],"as":[116],"input":[117],"produce":[119],"joint":[120],"encoding":[121],"(contraction)":[122],"expansion":[124],"paths":[125],"(decoding)":[126],"U-Net.":[129],"We":[130],"present":[131],"results":[132],"successfully":[134],"delineating":[135],"subdural":[136],"epidural":[138],"hematomas":[139],"brain":[141],"CT":[142,149],"imaging":[143],"liver":[145],"hemangioma":[146],"abdominal":[148],"network.":[153]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}