{"id":"https://openalex.org/W3011194076","doi":"https://doi.org/10.1117/12.2549363","title":"Learning efficient channels with a dual loss autoencoder","display_name":"Learning efficient channels with a dual loss autoencoder","publication_year":2020,"publication_date":"2020-03-16","ids":{"openalex":"https://openalex.org/W3011194076","doi":"https://doi.org/10.1117/12.2549363","mag":"3011194076"},"language":"en","primary_location":{"id":"doi:10.1117/12.2549363","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549363","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/A5068094970","display_name":"Jason Granstedt","orcid":"https://orcid.org/0009-0009-8228-5540"},"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":true,"raw_author_name":"Jason L. Granstedt","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/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":false,"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":"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":3,"corresponding_author_ids":["https://openalex.org/A5068094970"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":0.3977,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.67141787,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"10","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.8705000281333923,"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/T10320","display_name":"Neural Networks and Applications","score":0.8705000281333923,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8566199541091919},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.7237064838409424},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6299210786819458},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37771281599998474},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.27212613821029663}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8566199541091919},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.7237064838409424},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6299210786819458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37771281599998474},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27212613821029663},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2549363","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549363","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/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W4220775285"],"abstract_inverted_index":{"In":[0,188],"medical":[1],"imaging":[2,246],"systems,":[3],"task-based":[4,51],"metrics":[5],"have":[6,107,126],"been":[7,127],"advocated":[8],"as":[9,143],"a":[10,139,153,170,191,218],"means":[11],"of":[12,21,67,96,100,114,156,183,220,227],"evaluating":[13],"image":[14,86],"quality.":[15],"Mathematical":[16],"observers":[17],"are":[18,46],"one":[19],"method":[20,149,193],"computing":[22,54,81,104],"such":[23],"metrics.":[24],"Although":[25],"the":[26,44,55,65,68,72,77,82,98,101,105,116,144,148,157,165,181,225,237,244,249,257],"Bayesian":[27],"Ideal":[28],"Observer":[29,74],"(IO)":[30],"is":[31,36,57,71,150,163,186,204,230],"optimal":[32,60],"by":[33,129,137],"definition,":[34],"it":[35],"frequently":[37],"intractable":[38,90],"and":[39,88,213],"non-linear.":[40],"Linear":[41],"approximations":[42],"to":[43,49,134,161,216],"IO":[45,56],"sometimes":[47],"employed":[48],"obtain":[50],"statistics":[52],"when":[53,180,224],"infeasible.":[58],"The":[59,206],"linear":[61],"observer":[62],"for":[63,80,91,194],"maximizing":[64],"SNR":[66],"test":[69,166],"statistic":[70,167],"Hotelling":[73],"(HO).":[75],"However,":[76,175],"computational":[78,122],"cost":[79],"HO":[83,106],"increases":[84],"with":[85,110,200],"size":[87],"becomes":[89],"larger":[92],"images.":[93],"Channelized":[94],"methods":[95,242],"reducing":[97],"dimensionality":[99],"data":[102,136,185],"before":[103],"become":[108],"popular,":[109],"efficient":[111,221],"channels":[112,125,162,196],"capable":[113],"approximating":[115,164],"HO\u2019s":[117],"performance":[118],"at":[119],"significantly":[120],"reduced":[121],"cost.":[123],"State-of-the-art":[124],"learned":[128],"using":[130,169],"an":[131,198],"autoencoder":[132],"(AE)":[133],"encode":[135],"employing":[138],"known":[140],"signal":[141],"template":[142],"desired":[145],"reconstruction,":[146],"but":[147],"dependant":[151],"on":[152,243],"high-quality":[154],"estimate":[155],"signal.":[158],"An":[159],"alternative":[160],"directly":[168],"feed-forward":[171],"neural":[172],"network":[173,239],"(FFNN).":[174],"this":[176,189],"approach":[177],"can":[178],"overfit":[179],"amount":[182],"training":[184,228],"limited.":[187],"work,":[190],"generalized":[192],"learning":[195],"utilizing":[197],"AE":[199],"dual":[201],"losses":[202,215],"(AEDL)":[203],"proposed.":[205],"AEDL":[207,250],"framework":[208,251],"jointly":[209],"minimizes":[210],"both":[211],"task-specific":[212],"reconstruction":[214],"learn":[217],"set":[219],"channels,":[222],"even":[223],"number":[226],"images":[229],"relatively":[231],"small.":[232],"Preliminary":[233],"results":[234],"indicate":[235],"that":[236],"proposed":[238],"outperforms":[240],"state-of-the-art":[241],"selected":[245],"task.":[247],"Additionally,":[248],"suffers":[252],"from":[253],"less":[254],"overfitting":[255],"than":[256],"FFNN.":[258]},"counts_by_year":[{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
