{"id":"https://openalex.org/W4391308157","doi":"https://doi.org/10.1109/smc53992.2023.10394200","title":"ROC-Score-Based Ensemble Training for Multiple Deep Learning Modules in Classification Between Polyps and Non-Polyps in CT Colonography","display_name":"ROC-Score-Based Ensemble Training for Multiple Deep Learning Modules in Classification Between Polyps and Non-Polyps in CT Colonography","publication_year":2023,"publication_date":"2023-10-01","ids":{"openalex":"https://openalex.org/W4391308157","doi":"https://doi.org/10.1109/smc53992.2023.10394200"},"language":"en","primary_location":{"id":"doi:10.1109/smc53992.2023.10394200","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/smc53992.2023.10394200","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5050949810","display_name":"Kenji Suzuki","orcid":"https://orcid.org/0000-0002-3993-8309"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Kenji Suzuki","raw_affiliation_strings":["Biomedical Artificial Intelligence Research Unit (BMAI), Institute of Innovative Research, Tokyo Institute of Technology,Yokohama,Kanagawa,Japan,226-8503"],"affiliations":[{"raw_affiliation_string":"Biomedical Artificial Intelligence Research Unit (BMAI), Institute of Innovative Research, Tokyo Institute of Technology,Yokohama,Kanagawa,Japan,226-8503","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030721229","display_name":"Abraham H. Dachman","orcid":"https://orcid.org/0000-0002-7035-2752"},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abraham H. Dachman","raw_affiliation_strings":["The University of Chicago,Department of Radiology,Chicago,IL,USA,60637"],"affiliations":[{"raw_affiliation_string":"The University of Chicago,Department of Radiology,Chicago,IL,USA,60637","institution_ids":["https://openalex.org/I40347166"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5050949810"],"corresponding_institution_ids":["https://openalex.org/I114531698"],"apc_list":null,"apc_paid":null,"fwci":0.2363,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.62817301,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"2","issue":null,"first_page":"1647","last_page":"1651"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9955000281333923,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9955000281333923,"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":0.9840999841690063,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9735999703407288,"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":0.5898829102516174},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5683521628379822},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4899684488773346},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.44817861914634705},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.43411991000175476},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.4242463707923889},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41145792603492737},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4048040509223938},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32571133971214294}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5898829102516174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5683521628379822},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4899684488773346},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.44817861914634705},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.43411991000175476},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4242463707923889},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41145792603492737},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4048040509223938},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32571133971214294},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc53992.2023.10394200","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/smc53992.2023.10394200","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5}],"awards":[{"id":"https://openalex.org/G1716091338","display_name":null,"funder_award_id":"JPMJMI20B8","funder_id":"https://openalex.org/F4320338243","funder_display_name":"JST-Mirai Program"}],"funders":[{"id":"https://openalex.org/F4320338243","display_name":"JST-Mirai Program","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1601680203","https://openalex.org/W1965479476","https://openalex.org/W1994127966","https://openalex.org/W2006527136","https://openalex.org/W2014586311","https://openalex.org/W2023522838","https://openalex.org/W2041831159","https://openalex.org/W2103311002","https://openalex.org/W2114973443","https://openalex.org/W2127085684","https://openalex.org/W2128392486","https://openalex.org/W2138128938","https://openalex.org/W2141619730","https://openalex.org/W2146911148","https://openalex.org/W2166901122","https://openalex.org/W2731899572","https://openalex.org/W2801761532","https://openalex.org/W2889155944","https://openalex.org/W2919115771","https://openalex.org/W2920223366","https://openalex.org/W3048378651","https://openalex.org/W3109319544","https://openalex.org/W6631704356","https://openalex.org/W6636045111"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W4375867731","https://openalex.org/W3216976533","https://openalex.org/W2795259429","https://openalex.org/W2291489469","https://openalex.org/W2546503577"],"abstract_inverted_index":{"We":[0,25,44,62,113,138,189],"developed":[1],"an":[2,28,37,130],"automatic":[3],"ensemble":[4,175,195,214],"training":[5,102,123,176,196,200,215],"method":[6,177,216],"for":[7],"fusing":[8],"massive-training":[9],"artificial":[10],"neural":[11],"network":[12],"(MTANN)":[13],"deep-learning":[14],"modules":[15,117,142],"in":[16,22,76,85],"classification":[17],"between":[18,87],"polyps":[19,41,51,88,103,180],"and":[20,42,52,89,104],"non-polyps":[21,53,90,105],"CT":[23,158],"colonography.":[24],"started":[26],"from":[27,162,182],"initial":[29,38,48,60,69,93],"MTANN":[30,116],"module":[31,49,70,128,146],"that":[32,126,198,220],"had":[33],"been":[34],"trained":[35,47,114,225],"with":[36,106,118,143,173,197,201,213,226],"set":[39],"of":[40,58,67,83,101,109,122,136,151,157,170,186,193,199,207,210,221,235],"non-polyps.":[43,188],"applied":[45],"the":[46,56,59,64,68,81,92,97,119,174,187,191,194,208,222,233,236],"to":[50,54,71,147,219],"analyze":[55],"weakness":[57],"module.":[61,94],"arranged":[63],"output":[65],"scores":[66],"form":[72,148],"a":[73,133,144,149],"score":[74],"scale":[75],"receiver-operating-characteristic":[77],"(ROC)":[78],"space,":[79],"representing":[80],"\u201cdegree":[82],"difficulty\u201d":[84],"distinction":[86],"by":[91],"Based":[95],"on":[96],"score-space,":[98],"several":[99,115,120],"sets":[100,121],"different":[107],"degrees":[108],"difficulties":[110],"were":[111],"determined.":[112],"samples":[124],"so":[125],"each":[127],"became":[129],"expert":[131,141,171,211],"at":[132],"certain":[134],"level":[135],"difficulty.":[137],"then":[139],"combined":[140],"mixing":[145],"\u201cmixture":[150],"expert\u201d":[152],"MTANNs.":[153],"Our":[154],"database":[155],"consisted":[156],"colonography":[159],"datasets":[160],"acquired":[161],"100":[163],"patients,":[164],"including":[165],"26":[166],"polyps.":[167],"The":[168,205],"mixture":[169,209],"MTANNs":[172,212,224],"distinguished":[178],"all":[179],"correctly":[181],"more":[183],"than":[184],"50%":[185],"compared":[190],"effectiveness":[192],"manually":[202,227],"selected":[203,228],"cases.":[204],"performance":[206],"was":[217],"superior":[218],"\u201creference-standard\u201d":[223],"cases,":[229],"which":[230],"could":[231],"reduce":[232],"cost":[234],"manual":[237],"selection.":[238]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
