{"id":"https://openalex.org/W2699360047","doi":"https://doi.org/10.1109/isbi.2017.7950707","title":"Feature selection and thyroid nodule classification using transfer learning","display_name":"Feature selection and thyroid nodule classification using transfer learning","publication_year":2017,"publication_date":"2017-04-01","ids":{"openalex":"https://openalex.org/W2699360047","doi":"https://doi.org/10.1109/isbi.2017.7950707","mag":"2699360047"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2017.7950707","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2017.7950707","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100650142","display_name":"Tianjiao Liu","orcid":"https://orcid.org/0000-0003-2412-641X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianjiao Liu","raw_affiliation_strings":["Dept. of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103917302","display_name":"Shuaining Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuaining Xie","raw_affiliation_strings":["Dept. of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088931934","display_name":"Yukang Zhang","orcid":"https://orcid.org/0009-0007-4547-5877"},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yukang Zhang","raw_affiliation_strings":["National Cancer Center/Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Cancer Center/Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China","institution_ids":["https://openalex.org/I200296433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101579038","display_name":"Jing Yu","orcid":"https://orcid.org/0000-0002-2854-8620"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Yu","raw_affiliation_strings":["Colg. of Computer Science & Technology, Beijing Univ. of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Colg. of Computer Science & Technology, Beijing Univ. of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013428170","display_name":"Lijuan Niu","orcid":"https://orcid.org/0000-0002-0798-9750"},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lijuan Niu","raw_affiliation_strings":["National Cancer Center/Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Cancer Center/Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China","institution_ids":["https://openalex.org/I200296433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024318974","display_name":"Weidong Sun","orcid":"https://orcid.org/0000-0002-8931-8407"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weidong Sun","raw_affiliation_strings":["Dept. of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1096","last_page":"1099"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9980999827384949,"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/T10862","display_name":"AI in cancer detection","score":0.9980999827384949,"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":0.9641000032424927,"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/T11363","display_name":"Dental Radiography and Imaging","score":0.9412999749183655,"subfield":{"id":"https://openalex.org/subfields/3504","display_name":"Oral Surgery"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8172702789306641},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7547782063484192},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7353551387786865},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6657892465591431},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6494470238685608},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.6303414106369019},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5251724123954773},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4897139370441437},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.44743576645851135},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4384308457374573},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.438096284866333},{"id":"https://openalex.org/keywords/thyroid-nodules","display_name":"Thyroid nodules","score":0.4303196370601654},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4266194701194763},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2416723370552063},{"id":"https://openalex.org/keywords/thyroid","display_name":"Thyroid","score":0.09834560751914978}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8172702789306641},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7547782063484192},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7353551387786865},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6657892465591431},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6494470238685608},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.6303414106369019},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5251724123954773},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4897139370441437},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.44743576645851135},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4384308457374573},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.438096284866333},{"id":"https://openalex.org/C2779022025","wikidata":"https://www.wikidata.org/wiki/Q53829","display_name":"Thyroid nodules","level":3,"score":0.4303196370601654},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4266194701194763},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2416723370552063},{"id":"https://openalex.org/C526584372","wikidata":"https://www.wikidata.org/wiki/Q16399","display_name":"Thyroid","level":2,"score":0.09834560751914978},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2017.7950707","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2017.7950707","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.75,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W190437827","https://openalex.org/W1524680991","https://openalex.org/W1849729440","https://openalex.org/W1946635378","https://openalex.org/W1987627406","https://openalex.org/W2078275311","https://openalex.org/W2088644411","https://openalex.org/W2105465844","https://openalex.org/W2161381512","https://openalex.org/W2163605009","https://openalex.org/W2166473218","https://openalex.org/W2170005116","https://openalex.org/W2430776200","https://openalex.org/W2513506629","https://openalex.org/W2963173190","https://openalex.org/W3126122061","https://openalex.org/W6607693206","https://openalex.org/W6631498818","https://openalex.org/W6638875480","https://openalex.org/W6648737282","https://openalex.org/W6684116732","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W3034955165","https://openalex.org/W2094920358","https://openalex.org/W2041448692","https://openalex.org/W2247121321","https://openalex.org/W2391926582","https://openalex.org/W2087391438","https://openalex.org/W1966831329","https://openalex.org/W2316074893","https://openalex.org/W2020188645","https://openalex.org/W4390143830"],"abstract_inverted_index":{"Ultrasonography":[0],"is":[1,39,84,146,181],"a":[2,52,125,141,155],"valuable":[3],"diagnosis":[4,22,27],"method":[5,55,180],"for":[6,45,56,148],"thyroid":[7],"nodules.":[8],"Automatically":[9],"discriminating":[10],"benign":[11],"and":[12,71,116,164],"malignant":[13],"nodules":[14],"in":[15,36],"the":[16,26,61,75,87,132,149,159,175],"ultrasound":[17,57,88],"images":[18,58,172],"can":[19],"provide":[20],"aided":[21],"suggestions,":[23],"or":[24],"increase":[25],"accuracy":[28,176],"when":[29],"lack":[30],"of":[31,112,161,177],"experts.":[32],"The":[33],"core":[34],"problem":[35],"this":[37,46],"issue":[38],"how":[40],"to":[41,67,74,86,91,123,130,138],"capture":[42],"appropriate":[43],"features":[44,73,95,105,108,135],"specific":[47,72],"task.":[48],"Here,":[49],"we":[50,101],"propose":[51],"feature":[53,127,142,162,165],"extraction":[54],"based":[59],"on":[60,158,170],"convolution":[62],"neural":[63],"networks":[64],"(CNNs),":[65],"try":[66],"introduce":[68],"more":[69,136],"meaningful":[70],"classification.":[76],"A":[77],"CNN":[78],"model":[79],"trained":[80],"with":[81,106],"ImageNet":[82],"data":[83],"transferred":[85],"image":[89],"domain,":[90],"generate":[92],"semantic":[93],"deep":[94,104,134],"under":[96],"small":[97],"sample":[98],"condition.":[99],"Then,":[100],"combine":[102],"those":[103],"conventional":[107],"such":[109],"as":[110],"Histogram":[111],"Oriented":[113],"Gradient":[114],"(HOG)":[115],"Scale":[117],"Invariant":[118],"Feature":[119],"Transform":[120],"(SIFT)":[121],"together":[122],"form":[124],"hybrid":[126,150],"space.":[128],"Furthermore,":[129],"make":[131],"general":[133],"pertinent":[137],"our":[139,178],"problem,":[140],"subset":[143],"selection":[144],"process":[145],"employed":[147],"nodule":[151],"classification,":[152],"followed":[153],"by":[154,188],"detailed":[156],"discussion":[157],"influence":[160],"number":[163],"composition":[166],"method.":[167],"Experimental":[168],"results":[169],"1037":[171],"show":[173],"that":[174],"proposed":[179],"0.929,":[182],"which":[183],"outperforms":[184],"other":[185],"relative":[186],"methods":[187],"over":[189],"10%.":[190]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}