{"id":"https://openalex.org/W4229058052","doi":"https://doi.org/10.1145/3477314.3507144","title":"CheReS","display_name":"CheReS","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W4229058052","doi":"https://doi.org/10.1145/3477314.3507144"},"language":"en","primary_location":{"id":"doi:10.1145/3477314.3507144","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3477314.3507144","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3477314.3507144","source":{"id":"https://openalex.org/S4363608665","display_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3477314.3507144","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014745684","display_name":"Ashery Mbilinyi","orcid":"https://orcid.org/0009-0009-1372-9072"},"institutions":[{"id":"https://openalex.org/I1850255","display_name":"University of Basel","ror":"https://ror.org/02s6k3f65","country_code":"CH","type":"education","lineage":["https://openalex.org/I1850255"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Ashery Mbilinyi","raw_affiliation_strings":["University of Basel, Basel, Switzerland"],"affiliations":[{"raw_affiliation_string":"University of Basel, Basel, Switzerland","institution_ids":["https://openalex.org/I1850255"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034703920","display_name":"Heiko Schuldt","orcid":"https://orcid.org/0000-0001-9865-6371"},"institutions":[{"id":"https://openalex.org/I1850255","display_name":"University of Basel","ror":"https://ror.org/02s6k3f65","country_code":"CH","type":"education","lineage":["https://openalex.org/I1850255"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Heiko Schuldt","raw_affiliation_strings":["University of Basel, Basel, Switzerland"],"affiliations":[{"raw_affiliation_string":"University of Basel, Basel, Switzerland","institution_ids":["https://openalex.org/I1850255"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5014745684"],"corresponding_institution_ids":["https://openalex.org/I1850255"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05747643,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"669","last_page":"676"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9975000023841858,"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"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9975000023841858,"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/T10862","display_name":"AI in cancer detection","score":0.9883999824523926,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9836000204086304,"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/computer-science","display_name":"Computer science","score":0.7490506172180176},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6915453672409058},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6857813596725464},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.6762433052062988},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6204163432121277},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5183587670326233},{"id":"https://openalex.org/keywords/demographics","display_name":"Demographics","score":0.4928321838378906},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48293396830558777},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4374712109565735},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.433544397354126},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4171810746192932},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4161442220211029}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7490506172180176},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6915453672409058},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6857813596725464},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.6762433052062988},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6204163432121277},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5183587670326233},{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.4928321838378906},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48293396830558777},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4374712109565735},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.433544397354126},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4171810746192932},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4161442220211029},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3477314.3507144","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3477314.3507144","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3477314.3507144","source":{"id":"https://openalex.org/S4363608665","display_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3477314.3507144","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3477314.3507144","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3477314.3507144","source":{"id":"https://openalex.org/S4363608665","display_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5600000023841858,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335238","display_name":"Staatssekretariat f\u00fcr Bildung, Forschung und Innovation","ror":"https://ror.org/01kw63t33"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4229058052.pdf","grobid_xml":"https://content.openalex.org/works/W4229058052.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W2077858109","https://openalex.org/W2083406700","https://openalex.org/W2137616970","https://openalex.org/W2568740995","https://openalex.org/W2601707599","https://openalex.org/W2611650229","https://openalex.org/W2901159198","https://openalex.org/W2939001550","https://openalex.org/W2963446712","https://openalex.org/W2963466845","https://openalex.org/W2964052837","https://openalex.org/W2985309991","https://openalex.org/W3041474434","https://openalex.org/W3090301213","https://openalex.org/W3101156210","https://openalex.org/W3191065828","https://openalex.org/W3203488456","https://openalex.org/W4297933831","https://openalex.org/W6717127683"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2752972570","https://openalex.org/W4297051394","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W2909431601","https://openalex.org/W4294770367"],"abstract_inverted_index":{"One":[0],"of":[1,5,10,84],"the":[2,8,29,69,82,181],"fundamental":[3],"tasks":[4],"radiologists":[6,241],"is":[7,17],"interpretation":[9],"X-ray":[11,136,207,226],"images.":[12],"To":[13,80],"do":[14,66],"this,":[15],"it":[16],"essential":[18],"to":[19,39,76,133,148,169,179,229],"search":[20],"for":[21,50,239],"similar":[22,97,184,225],"cases":[23,98,186,227],"that":[24,59,94,121,166,187,215],"would":[25,188],"help":[26,68,190],"them":[27],"in":[28,71,202,221,242],"decision-making":[30],"process,":[31],"especially":[32],"when":[33],"they":[34],"face":[35],"an":[36,128,163],"ambiguous":[37,113,154],"image":[38],"interpret.":[40],"Traditionally,":[41],"Content-Based":[42],"Medical":[43],"Image":[44],"Retrieval":[45],"(CBMIR)":[46],"has":[47,217],"been":[48],"applied":[49],"this":[51,86],"task.":[52],"However,":[53],"CBMIR":[54,232],"systems":[55],"sometimes":[56],"retrieve":[57],"images":[58,155],"are":[60],"not":[61,67],"clinically":[62,96],"relevant":[63],"and":[64,108,115,140,176,210,223],"thus":[65],"radiologist":[70,192],"their":[72,78,116,243],"comparative":[73],"analysis":[74],"needed":[75],"back":[77],"decision.":[79],"tackle":[81],"limitations":[83],"CBMIR,":[85],"paper":[87],"introduces":[88],"CheReS,":[89],"a":[90,141,158,191,194,218,230,236],"novel":[91],"multi-faceted":[92],"approach":[93],"retrieves":[95],"by":[99,122,157,173],"taking":[100],"into":[101],"account":[102],"patients'":[103,177],"demographics":[104,178],"(such":[105],"as":[106],"age":[107],"gender),":[109],"disease":[110],"predictions":[111],"on":[112,153],"images,":[114],"visual":[117,137],"contents.":[118],"CheReS":[119,201,216],"accomplishes":[120],"employing":[123],"two":[124,203],"deep":[125],"learning":[126],"models:":[127],"unsupervised":[129],"Autoencoder":[130],"we":[131,146,161],"trained":[132,147],"learn":[134],"low-dimensional":[135],"feature":[138],"representations":[139],"supervised":[142],"Convolutional":[143],"Neural":[144],"Network":[145],"predict":[149],"common":[150],"chest":[151,206],"diseases":[152],"submitted":[156],"radiologist.":[159],"Lastly,":[160],"employed":[162],"algorithmic":[164],"procedure":[165],"decides":[167],"how":[168],"leverage":[170],"information":[171],"delivered":[172],"both":[174],"models":[175],"identify":[180],"most":[182],"accurate":[183],"X-rays":[185],"significantly":[189],"diagnose":[193],"case":[195],"at":[196],"hand.":[197],"We":[198],"have":[199],"evaluated":[200],"publicly":[204],"available":[205],"datasets,":[208],"CheXpert":[209],"ChestX-ray14.":[211],"Our":[212],"results":[213],"show":[214],"significant":[219],"gain":[220],"identifying":[222],"retrieving":[224],"compared":[228],"traditional":[231],"approach,":[233],"therefore,":[234],"providing":[235],"better":[237],"solution":[238],"augmenting":[240],"workflow.":[244]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2022-05-08T00:00:00"}
