{"id":"https://openalex.org/W3010923835","doi":"https://doi.org/10.1117/12.2550531","title":"Hyperparameter selection for ResNet classification of malignancy from thyroid ultrasound images","display_name":"Hyperparameter selection for ResNet classification of malignancy from thyroid ultrasound images","publication_year":2020,"publication_date":"2020-03-16","ids":{"openalex":"https://openalex.org/W3010923835","doi":"https://doi.org/10.1117/12.2550531","mag":"3010923835"},"language":"en","primary_location":{"id":"doi:10.1117/12.2550531","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2550531","pdf_url":null,"source":{"id":"https://openalex.org/S4306519512","display_name":"Medical Imaging 2020: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Computer-Aided Diagnosis","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/A5090256094","display_name":"J. Cox","orcid":"https://orcid.org/0000-0001-8689-3310"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph Cox","raw_affiliation_strings":["Purdue Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue Univ. (United States)","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082079171","display_name":"Sydney Rubin","orcid":null},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sydney Rubin","raw_affiliation_strings":["Michigan State Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Michigan State Univ. (United States)","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027047524","display_name":"Joe Adams","orcid":null},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joe Adams","raw_affiliation_strings":["Michigan State Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Michigan State Univ. (United States)","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065295670","display_name":"Carina Barbosa Pereira","orcid":"https://orcid.org/0000-0003-1788-4562"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carina Pereira","raw_affiliation_strings":["Univ. of Washington (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Washington (United States)","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057636900","display_name":"Manjiri Dighe","orcid":"https://orcid.org/0000-0003-0047-5148"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Manjiri Dighe","raw_affiliation_strings":["Univ. of Washington (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Washington (United States)","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077835961","display_name":"Adam Alessio","orcid":"https://orcid.org/0000-0003-3371-8580"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adam Alessio","raw_affiliation_strings":["Michigan State Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Michigan State Univ. (United States)","institution_ids":["https://openalex.org/I87216513"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2744,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.57211943,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"149","last_page":"149"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10329","display_name":"Thyroid Cancer Diagnosis and Treatment","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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/T10329","display_name":"Thyroid Cancer Diagnosis and Treatment","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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":0.9628999829292297,"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":0.9287999868392944,"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/hyperparameter","display_name":"Hyperparameter","score":0.7826018333435059},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6574375033378601},{"id":"https://openalex.org/keywords/thyroid-nodules","display_name":"Thyroid nodules","score":0.6329616904258728},{"id":"https://openalex.org/keywords/malignancy","display_name":"Malignancy","score":0.5539851188659668},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5407741665840149},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4922894537448883},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.48352789878845215},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4571225047111511},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.4543156325817108},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.29055055975914},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.18969368934631348},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.12047696113586426}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7826018333435059},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6574375033378601},{"id":"https://openalex.org/C2779022025","wikidata":"https://www.wikidata.org/wiki/Q53829","display_name":"Thyroid nodules","level":3,"score":0.6329616904258728},{"id":"https://openalex.org/C2779399171","wikidata":"https://www.wikidata.org/wiki/Q1483951","display_name":"Malignancy","level":2,"score":0.5539851188659668},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5407741665840149},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4922894537448883},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.48352789878845215},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4571225047111511},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.4543156325817108},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.29055055975914},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.18969368934631348},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.12047696113586426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2550531","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2550531","pdf_url":null,"source":{"id":"https://openalex.org/S4306519512","display_name":"Medical Imaging 2020: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.6600000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2953665647","https://openalex.org/W4281646320","https://openalex.org/W4205712847","https://openalex.org/W3169687406","https://openalex.org/W4388119537","https://openalex.org/W3014750173","https://openalex.org/W3114025147","https://openalex.org/W4287818966","https://openalex.org/W3192751261","https://openalex.org/W3200811867"],"abstract_inverted_index":{"Thyroid":[0,70],"nodules":[1,24,50,168],"are":[2,51],"extremely":[3],"common,":[4],"with":[5,117,169,178,183],"a":[6,42,82,110,132,149,162,217,225],"prevalence":[7],"of":[8,35,59,167,175],"up":[9],"to":[10,19,39,55,77,97,152,193,199,227],"68%":[11],"in":[12,32,161],"adults.":[13],"Ultrasound":[14],"imaging":[15],"is":[16,44,87,91],"usually":[17],"performed":[18,148],"detect":[20],"and":[21,73,81,94,101,119,143,172,224],"evaluate":[22],"thyroid":[23,221],"for":[25,157,220],"malignancy.":[26],"Many":[27],"patients":[28],"undergo":[29],"follow-up":[30],"biopsy":[31],"the":[33,57,154,180,187],"form":[34],"fine-needle":[36],"aspiration":[37],"(FNA)":[38],"determine":[40,153],"if":[41],"nodule":[43,222],"malignant":[45],"or":[46],"benign,":[47],"although":[48],"most":[49],"benign.":[52],"In":[53],"order":[54],"reduce":[56],"number":[58],"unnecessary":[60],"FNAs,":[61],"radiologists":[62,100],"will":[63],"often":[64],"use":[65],"classification":[66,185,223],"systems":[67],"such":[68],"as":[69],"Imaging,":[71],"Reporting,":[72],"Data":[74],"System":[75],"(TI-RADS)":[76],"provide":[78],"risk":[79],"stratification":[80],"recommendation":[83],"regarding":[84],"whether":[85],"FNA":[86],"necessary.":[88],"This":[89],"scoring":[90],"both":[92],"subjective":[93],"time-consuming,":[95],"leading":[96],"discrepancies":[98],"between":[99],"recommendations":[102],"that":[103,109,164,197,213],"can":[104,114],"be":[105,115],"inaccurate.":[106],"We":[107,130,147],"hypothesize":[108],"machine":[111],"learned":[112],"classifier":[113],"identified":[116],"accurate":[118],"generalizable":[120,201,231],"performance,":[121],"potentially":[122],"offering":[123],"more":[124,200,230],"consistent":[125],"results":[126,211],"than":[127],"manual":[128],"evaluation.":[129],"created":[131],"network":[133,163],"from":[134],"two":[135,139],"ResNet-50":[136],"branches":[137],"accepting":[138],"inputs,":[140],"shear-wave":[141],"elastography":[142],"B-mode":[144],"ultrasound":[145],"images.":[146],"grid":[150,188],"search":[151,189],"optimal":[155,184],"hyperparameters":[156,182],"our":[158,214],"model,":[159],"resulting":[160],"predicted":[165],"malignancy":[166],"88.7%":[170],"accuracy":[171],"an":[173],"AUC":[174],"0.91.":[176],"Along":[177],"identifying":[179],"training":[181,195],"accuracy,":[186],"also":[190],"allowed":[191],"us":[192],"select":[194],"parameters":[196],"led":[198],"model":[202,215],"performance":[203,210],"on":[204],"test":[205],"data":[206],"sets.":[207],"These":[208],"initial":[209],"suggest":[212],"offers":[216],"promising":[218],"strategy":[219,226],"help":[228],"identify":[229],"models.":[232]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}