{"id":"https://openalex.org/W3012497648","doi":"https://doi.org/10.1117/12.2549610","title":"Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements","display_name":"Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements","publication_year":2020,"publication_date":"2020-03-16","ids":{"openalex":"https://openalex.org/W3012497648","doi":"https://doi.org/10.1117/12.2549610","mag":"3012497648"},"language":"en","primary_location":{"id":"doi:10.1117/12.2549610","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549610","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment","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/A5035698435","display_name":"Weimin Zhou","orcid":"https://orcid.org/0000-0001-6678-4088"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Weimin Zhou","raw_affiliation_strings":["Washington Univ. in St. Louis (United States)"],"affiliations":[{"raw_affiliation_string":"Washington Univ. in St. Louis (United States)","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091328277","display_name":"Sayantan Bhadra","orcid":"https://orcid.org/0000-0001-8321-1142"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sayantan Bhadra","raw_affiliation_strings":["Washington Univ. in St. Louis (United States)"],"affiliations":[{"raw_affiliation_string":"Washington Univ. in St. Louis (United States)","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064714231","display_name":"Frank J. Brooks","orcid":"https://orcid.org/0000-0002-5949-3386"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Frank J. Brooks","raw_affiliation_strings":["Univ. of Illinois at Urbana-Champaign (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Illinois at Urbana-Champaign (United States)","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078094074","display_name":"Hua Li","orcid":"https://orcid.org/0000-0003-0740-0691"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hua Li","raw_affiliation_strings":["Univ. of Illinois at Urbana-Champaign (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Illinois at Urbana-Champaign (United States)","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046506193","display_name":"Mark A. Anastasio","orcid":"https://orcid.org/0000-0002-3192-4172"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark A. Anastasio","raw_affiliation_strings":["Univ. of Illinois at Urbana-Champaign (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Illinois at Urbana-Champaign (United States)","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5035698435"],"corresponding_institution_ids":["https://openalex.org/I204465549"],"apc_list":null,"apc_paid":null,"fwci":0.3908,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.60116287,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"24","last_page":"24"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9976000189781189,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9976000189781189,"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/T10862","display_name":"AI in cancer detection","score":0.9914000034332275,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9804999828338623,"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":0.7865511178970337},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6470081806182861},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6188753247261047},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.5740468502044678},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.5109381079673767},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.46379074454307556},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.43323689699172974},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.41738906502723694},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41547682881355286},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37571343779563904},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3655148446559906},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.36080068349838257},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0928536057472229}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7865511178970337},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6470081806182861},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6188753247261047},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.5740468502044678},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.5109381079673767},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.46379074454307556},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.43323689699172974},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.41738906502723694},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41547682881355286},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37571343779563904},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3655148446559906},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36080068349838257},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0928536057472229},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2549610","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549610","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4395044357","https://openalex.org/W4287117424","https://openalex.org/W4387506531","https://openalex.org/W2087346071","https://openalex.org/W2967848559","https://openalex.org/W4299831724"],"abstract_inverted_index":{"The":[0,188],"objective":[1],"optimization":[2],"of":[3,10,13,26,47,77,94,106,140,157,163,201],"medical":[4,82],"imaging":[5,83,86,172,179],"systems":[6,84],"requires":[7],"full":[8],"characterization":[9],"all":[11],"sources":[12],"randomness":[14],"in":[15,44],"the":[16,21,24,42,45,72,132,138,141,161,209],"measured":[17],"data,":[18],"which":[19],"includes":[20],"variability":[22,43,73],"within":[23,74],"ensemble":[25,76],"objects":[27,48,107],"to-be-imaged.":[28,49,108],"This":[29],"can":[30,54,135,143],"be":[31,55,99,136,144],"accomplished":[32],"by":[33,193,199],"establishing":[34,166],"a":[35,152],"stochastic":[36,104],"object":[37,95,211],"model":[38],"(SOM)":[39],"that":[40,70,88,207],"describes":[41],"class":[46],"Generative":[50],"adversarial":[51,133],"networks":[52],"(GANs)":[53],"potentially":[56,145],"useful":[57],"to":[58,66,102,121],"establish":[59,103,122],"SOMs":[60,123,167],"because":[61,81,131],"they":[62],"hold":[63],"great":[64],"promise":[65],"learn":[67],"generative":[68],"models":[69,105],"describe":[71],"an":[75,113],"training":[78,134,154,162],"data.":[79,129],"However,":[80,130],"record":[85],"measurements":[87],"are":[89,186],"noisy":[90,125,169],"and":[91,126,170,181,205],"indirect":[92,127,171],"representations":[93],"properties,":[96],"GANs":[97],"cannot":[98],"directly":[100],"applied":[101],"To":[109],"address":[110],"this":[111,148],"issue,":[112],"augmented":[114],"GAN":[115],"architecture":[116],"named":[117],"AmbientGAN":[118,142],"was":[119],"developed":[120],"from":[124,168],"measurement":[128],"unstable,":[137],"applicability":[139],"limited.":[146],"In":[147],"work,":[149],"we":[150],"propose":[151],"novel":[153],"strategy|Progressive":[155],"Growing":[156],"AmbientGANs":[158,164],"(ProAGAN)|to":[159],"stabilize":[160],"for":[165],"measurements.":[173],"An":[174],"idealized":[175],"magnetic":[176],"resonance":[177],"(MR)":[178],"system":[180],"clinical":[182],"MR":[183],"brain":[184],"images":[185,204,206],"considered.":[187],"proposed":[189],"methodology":[190],"is":[191],"evaluated":[192],"comparing":[194],"signal":[195],"detection":[196],"performance":[197],"computed":[198],"use":[200],"ProAGAN-generated":[202],"synthetic":[203],"depict":[208],"true":[210],"properties.":[212]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}