{"id":"https://openalex.org/W3195491223","doi":"https://doi.org/10.1145/3463944.3469100","title":"Pyramidal Segmentation of Medical Images using Adversarial Training","display_name":"Pyramidal Segmentation of Medical Images using Adversarial Training","publication_year":2021,"publication_date":"2021-08-20","ids":{"openalex":"https://openalex.org/W3195491223","doi":"https://doi.org/10.1145/3463944.3469100","mag":"3195491223"},"language":"en","primary_location":{"id":"doi:10.1145/3463944.3469100","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3463944.3469100","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","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/A5004014426","display_name":"Espen N\u00e6ss","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153474","display_name":"Simula Metropolitan Center for Digital Engineering","ror":"https://ror.org/04xtarr15","country_code":"NO","type":"nonprofit","lineage":["https://openalex.org/I184531372","https://openalex.org/I2799829267","https://openalex.org/I4210153474"]}],"countries":["NO"],"is_corresponding":true,"raw_author_name":"Espen Naess","raw_affiliation_strings":["SimulaMet, Oslo, Norway"],"affiliations":[{"raw_affiliation_string":"SimulaMet, Oslo, Norway","institution_ids":["https://openalex.org/I4210153474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034317141","display_name":"Vajira Thambawita","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153474","display_name":"Simula Metropolitan Center for Digital Engineering","ror":"https://ror.org/04xtarr15","country_code":"NO","type":"nonprofit","lineage":["https://openalex.org/I184531372","https://openalex.org/I2799829267","https://openalex.org/I4210153474"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Vajira Thambawita","raw_affiliation_strings":["SimulaMet, Oslo, Norway"],"affiliations":[{"raw_affiliation_string":"SimulaMet, Oslo, Norway","institution_ids":["https://openalex.org/I4210153474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022601521","display_name":"Steven A. Hicks","orcid":"https://orcid.org/0000-0002-3332-1201"},"institutions":[{"id":"https://openalex.org/I4210153474","display_name":"Simula Metropolitan Center for Digital Engineering","ror":"https://ror.org/04xtarr15","country_code":"NO","type":"nonprofit","lineage":["https://openalex.org/I184531372","https://openalex.org/I2799829267","https://openalex.org/I4210153474"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Steven A. Hicks","raw_affiliation_strings":["SimulaMet, Oslo, Norway"],"affiliations":[{"raw_affiliation_string":"SimulaMet, Oslo, Norway","institution_ids":["https://openalex.org/I4210153474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102968267","display_name":"Michael A. Riegler","orcid":"https://orcid.org/0000-0002-3153-2064"},"institutions":[{"id":"https://openalex.org/I4210153474","display_name":"Simula Metropolitan Center for Digital Engineering","ror":"https://ror.org/04xtarr15","country_code":"NO","type":"nonprofit","lineage":["https://openalex.org/I184531372","https://openalex.org/I2799829267","https://openalex.org/I4210153474"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Michael A. Riegler","raw_affiliation_strings":["SimulaMet, Oslo, Norway"],"affiliations":[{"raw_affiliation_string":"SimulaMet, Oslo, Norway","institution_ids":["https://openalex.org/I4210153474"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042166457","display_name":"Paal Halvorsen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153474","display_name":"Simula Metropolitan Center for Digital Engineering","ror":"https://ror.org/04xtarr15","country_code":"NO","type":"nonprofit","lineage":["https://openalex.org/I184531372","https://openalex.org/I2799829267","https://openalex.org/I4210153474"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Paal Halvorsen","raw_affiliation_strings":["SimulaMet, Oslo, Norway"],"affiliations":[{"raw_affiliation_string":"SimulaMet, Oslo, Norway","institution_ids":["https://openalex.org/I4210153474"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5004014426"],"corresponding_institution_ids":["https://openalex.org/I4210153474"],"apc_list":null,"apc_paid":null,"fwci":0.089,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.46404082,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"33","last_page":"38"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9998999834060669,"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":0.9988999962806702,"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":0.9911999702453613,"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/segmentation","display_name":"Segmentation","score":0.7703374028205872},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6731618642807007},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6464163661003113},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.57399582862854},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.48704293370246887},{"id":"https://openalex.org/keywords/colonoscopy","display_name":"Colonoscopy","score":0.43328163027763367},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4125415086746216},{"id":"https://openalex.org/keywords/colorectal-cancer","display_name":"Colorectal cancer","score":0.39347925782203674},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3734769821166992},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36420804262161255},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2972033619880676},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.26807844638824463},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1715218722820282},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.09629261493682861}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7703374028205872},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6731618642807007},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6464163661003113},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.57399582862854},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.48704293370246887},{"id":"https://openalex.org/C2778435480","wikidata":"https://www.wikidata.org/wiki/Q840387","display_name":"Colonoscopy","level":4,"score":0.43328163027763367},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4125415086746216},{"id":"https://openalex.org/C526805850","wikidata":"https://www.wikidata.org/wiki/Q188874","display_name":"Colorectal cancer","level":3,"score":0.39347925782203674},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3734769821166992},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36420804262161255},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2972033619880676},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.26807844638824463},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1715218722820282},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.09629261493682861},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3463944.3469100","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3463944.3469100","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8500000238418579,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1517389415","https://openalex.org/W1901129140","https://openalex.org/W2008359794","https://openalex.org/W2063979222","https://openalex.org/W2070210159","https://openalex.org/W2469346318","https://openalex.org/W2552465644","https://openalex.org/W2597760843","https://openalex.org/W2605287558","https://openalex.org/W2780161045","https://openalex.org/W2788745520","https://openalex.org/W2807271788","https://openalex.org/W2883707521","https://openalex.org/W2887719255","https://openalex.org/W2889646458","https://openalex.org/W2899264098","https://openalex.org/W2944939720","https://openalex.org/W2982224150","https://openalex.org/W2983365766","https://openalex.org/W3016181437","https://openalex.org/W3034279358","https://openalex.org/W3104061658","https://openalex.org/W3120333390","https://openalex.org/W3153821979","https://openalex.org/W3164631742","https://openalex.org/W4286411622","https://openalex.org/W4287067367","https://openalex.org/W6748628433","https://openalex.org/W6770612327"],"related_works":["https://openalex.org/W2381279477","https://openalex.org/W3016587774","https://openalex.org/W2999920852","https://openalex.org/W3028983594","https://openalex.org/W2006998504","https://openalex.org/W2363227174","https://openalex.org/W2088704621","https://openalex.org/W2073857279","https://openalex.org/W2071852359","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Colorectal":[0],"cancer":[1,37],"is":[2,17,27,88],"a":[3,9,31,89,113],"severe":[4],"health":[5],"issue":[6],"globally":[7],"and":[8,49,85,129,141,151,188],"significant":[10],"cause":[11],"of":[12,51,56,72,80,167],"cancer-related":[13],"mortality,":[14],"but":[15],"it":[16],"treatable":[18],"if":[19],"found":[20],"at":[21,158,164],"an":[22],"early":[23],"stage.":[24],"Early":[25],"detection":[26],"usually":[28],"done":[29],"through":[30],"colonoscopy,":[32],"where":[33,92,176],"clinicians":[34,45,67],"search":[35],"for":[36,83,106,148,173],"precursors":[38],"called":[39],"polyps.":[40,74],"Research":[41],"has":[42,95],"shown":[43],"that":[44,156],"miss":[46],"between":[47],"14%":[48],"30%":[50],"polyps":[52,63,87],"during":[53],"standard":[54],"screenings":[55],"the":[57,62,70,73,165,174],"gastrointestinal":[58],"tract.":[59],"Furthermore,":[60],"once":[61],"have":[64,103,135],"been":[65],"found,":[66],"often":[68],"overestimate":[69],"size":[71],"In":[75,108,132],"this":[76,109],"respect,":[77],"automatic":[78],"analysis":[79],"medical":[81],"images":[82],"detecting":[84],"locating":[86],"research":[90],"area":[91],"machine":[93],"learning":[94,118],"excelled":[96],"in":[97],"recent":[98],"years.":[99],"Still,":[100],"current":[101],"models":[102],"much":[104],"room":[105],"improvement.":[107],"paper,":[110],"we":[111,125,134],"propose":[112],"novel":[114],"approach":[115],"based":[116],"on":[117],"to":[119,127,185],"segment":[120],"within":[121],"several":[122,138],"grids,":[123],"which":[124,170],"introduce":[126],"U-Net":[128,187],"Pix2Pix":[130],"architectures.":[131],"short,":[133],"experimented":[136],"using":[137,142],"grid":[139],"sizes,":[140],"two":[143],"open-source":[144],"polyp":[145],"segmentation":[146,157,194],"datasets":[147],"cross-data":[149],"training":[150],"testing.":[152],"Our":[153],"results":[154,163],"suggest":[155],"lower":[159],"resolutions":[160],"produces":[161],"better":[162],"cost":[166],"less":[168],"precision,":[169],"proved":[171],"useful":[172],"cases":[175],"higher":[177],"precision":[178],"segmentations":[179],"gave":[180],"limited":[181],"results.":[182],"Generally,":[183],"compared":[184],"traditional":[186],"Pix2Pix,":[189],"our":[190],"grid-based":[191],"approaches":[192],"improve":[193],"performance.":[195]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
