{"id":"https://openalex.org/W2343973580","doi":"https://doi.org/10.1109/tmi.2016.2526687","title":"Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines","display_name":"Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines","publication_year":2016,"publication_date":"2016-02-08","ids":{"openalex":"https://openalex.org/W2343973580","doi":"https://doi.org/10.1109/tmi.2016.2526687","mag":"2343973580","pmid":"https://pubmed.ncbi.nlm.nih.gov/26886968"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2016.2526687","doi":"https://doi.org/10.1109/tmi.2016.2526687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2016.2526687","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["ccf-b","cwts-core","dongbi-a","doyens","jufo-3","ki-jl-2","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure.eur.nl/en/publications/31da334f-dc41-4690-b19f-b72315d49698","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072434846","display_name":"Gijs van Tulder","orcid":"https://orcid.org/0000-0003-1635-5423","observed_orcids":["https://orcid.org/0000-0003-1635-5423"]},"institutions":[{"id":"https://openalex.org/I2801952686","display_name":"Erasmus MC","ror":"https://ror.org/018906e22","country_code":"NL","type":"healthcare","lineage":["https://openalex.org/I2801952686"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Gijs van Tulder","raw_affiliation_strings":["Erasmus MC, Biomedical Imaging Group, Rotterdam, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Erasmus MC, Biomedical Imaging Group, Rotterdam, The Netherlands","institution_ids":["https://openalex.org/I2801952686"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023677285","display_name":"Marleen de Bruijne","orcid":"https://orcid.org/0000-0002-6328-902X","observed_orcids":["https://orcid.org/0000-0002-6328-902X"]},"institutions":[{"id":"https://openalex.org/I124055696","display_name":"University of Copenhagen","ror":"https://ror.org/035b05819","country_code":"DK","type":"education","lineage":["https://openalex.org/I124055696"]},{"id":"https://openalex.org/I2801952686","display_name":"Erasmus MC","ror":"https://ror.org/018906e22","country_code":"NL","type":"healthcare","lineage":["https://openalex.org/I2801952686"]}],"countries":["DK","NL"],"is_corresponding":false,"raw_author_name":"Marleen de Bruijne","raw_affiliation_strings":["Department of Computer Science, University of Copenhagen, Denmark","Erasmus MC, Biomedical Imaging Group, Rotterdam, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Copenhagen, Denmark","institution_ids":["https://openalex.org/I124055696"]},{"raw_affiliation_string":"Erasmus MC, Biomedical Imaging Group, Rotterdam, The Netherlands","institution_ids":["https://openalex.org/I2801952686"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":15.49,"has_fulltext":true,"cited_by_count":144,"citation_normalized_percentile":{"value":0.99520461,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"35","issue":"5","first_page":"1262","last_page":"1272"},"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":0.663100004196167,"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":0.663100004196167,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.07329999655485153,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.059300001710653305,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/convolutional-restricted-boltzmann-machine","display_name":"convolutional restricted Boltzmann machine","score":0.8640000224113464},{"id":"https://openalex.org/keywords/representation-learning","display_name":"representation learning","score":0.8949999809265137},{"id":"https://openalex.org/keywords/lung-tissue-classification","display_name":"lung tissue classification","score":0.7620000243186951},{"id":"https://openalex.org/keywords/lung-ct","display_name":"lung CT","score":0.6669999957084656},{"id":"https://openalex.org/keywords/unsupervised-representation-learning","display_name":"unsupervised feature learning","score":0.7509999871253967},{"id":"https://openalex.org/keywords/texture-classification","display_name":"texture classification","score":0.5879999995231628}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8999122381210327},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7658342123031616},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6773083209991455},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6693949699401855},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.6535738110542297},{"id":"https://openalex.org/C199354608","wikidata":"https://www.wikidata.org/wiki/Q7316287","display_name":"Restricted Boltzmann machine","level":3,"score":0.6241579651832581},{"id":"https://openalex.org/C192576344","wikidata":"https://www.wikidata.org/wiki/Q194706","display_name":"Boltzmann machine","level":3,"score":0.6176356077194214},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6161638498306274},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5855319499969482},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5639641284942627},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5236841440200806},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5053146481513977},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4343080520629883},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3584133982658386},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.22341841459274292},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.07852566242218018},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008168","descriptor_name":"Lung","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":7,"locations":[{"id":"doi:10.1109/tmi.2016.2526687","doi":"https://doi.org/10.1109/tmi.2016.2526687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2016.2526687","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["ccf-b","cwts-core","dongbi-a","doyens","jufo-3","ki-jl-2","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},{"id":"pmid:26886968","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/26886968","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on medical imaging","raw_type":"Journal Article"},{"id":"pmh:oai:pure.eur.nl:openaire/31da334f-dc41-4690-b19f-b72315d49698","doi":null,"is_oa":true,"landing_page_url":"https://pure.eur.nl/en/publications/31da334f-dc41-4690-b19f-b72315d49698","pdf_url":null,"source":{"id":"https://openalex.org/S4306401266","display_name":"EUR Research Repository (Erasmus University Rotterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I913958620","host_organization_name":"Erasmus University Rotterdam","host_organization_lineage":["https://openalex.org/I913958620"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"van Tulder, G & de Bruijne, M 2016, 'Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines', IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1262-1272. https://doi.org/10.1109/tmi.2016.2526687","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:eur:oai:pure.eur.nl:publications/31da334f-dc41-4690-b19f-b72315d49698","doi":null,"is_oa":false,"landing_page_url":"https://pure.eur.nl/en/publications/31da334f-dc41-4690-b19f-b72315d49698","pdf_url":null,"source":{"id":"https://openalex.org/S4306401266","display_name":"EUR Research Repository (Erasmus University Rotterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I913958620","host_organization_name":"Erasmus University Rotterdam","host_organization_lineage":["https://openalex.org/I913958620"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Medical Imaging, 35(5), 1262 - 1272. Institute of Electrical and Electronics Engineers Inc.","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:eur:oai:repub.eur.nl:80096","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/1765/80096","pdf_url":null,"source":{"id":"https://openalex.org/S4377196287","display_name":"RePub (Erasmus University Rotterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I913958620","host_organization_name":"Erasmus University Rotterdam","host_organization_lineage":["https://openalex.org/I913958620"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Medical Imaging, 35(5), 1262 - 1272","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/3c84f42e-283f-4cb1-89ec-6fbf9560badd","doi":null,"is_oa":false,"landing_page_url":"https://researchprofiles.ku.dk/da/publications/3c84f42e-283f-4cb1-89ec-6fbf9560badd","pdf_url":null,"source":{"id":"https://openalex.org/S4306401983","display_name":"Research at the University of Copenhagen (University of Copenhagen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I124055696","host_organization_name":"University of Copenhagen","host_organization_lineage":["https://openalex.org/I124055696"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"van Tulder, G & de Bruijne, M 2016, 'Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines', IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1262-1272. https://doi.org/10.1109/TMI.2016.2526687","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:repub.eur.nl:80096","doi":null,"is_oa":false,"landing_page_url":"https://repub.eur.nl/pub/80096","pdf_url":null,"source":{"id":"https://openalex.org/S4377196287","display_name":"RePub (Erasmus University Rotterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I913958620","host_organization_name":"Erasmus University Rotterdam","host_organization_lineage":["https://openalex.org/I913958620"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Medical Imaging vol. 35 no. 5, pp. 1262-1272","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:pure.eur.nl:openaire/31da334f-dc41-4690-b19f-b72315d49698","doi":null,"is_oa":true,"landing_page_url":"https://pure.eur.nl/en/publications/31da334f-dc41-4690-b19f-b72315d49698","pdf_url":null,"source":{"id":"https://openalex.org/S4306401266","display_name":"EUR Research Repository (Erasmus University Rotterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I913958620","host_organization_name":"Erasmus University Rotterdam","host_organization_lineage":["https://openalex.org/I913958620"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"van Tulder, G & de Bruijne, M 2016, 'Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines', IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1262-1272. https://doi.org/10.1109/tmi.2016.2526687","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G7637995368","display_name":null,"funder_award_id":"639.022.010","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W9958333","https://openalex.org/W44815768","https://openalex.org/W78356000","https://openalex.org/W317170363","https://openalex.org/W948663339","https://openalex.org/W1422227952","https://openalex.org/W1484228140","https://openalex.org/W1516407868","https://openalex.org/W1518102357","https://openalex.org/W1604956872","https://openalex.org/W1898227994","https://openalex.org/W1971014294","https://openalex.org/W1973979053","https://openalex.org/W1975020933","https://openalex.org/W1975815454","https://openalex.org/W1978070069","https://openalex.org/W2002471533","https://openalex.org/W2015450448","https://openalex.org/W2031902571","https://openalex.org/W2032281442","https://openalex.org/W2039051707","https://openalex.org/W2044097773","https://openalex.org/W2051775772","https://openalex.org/W2059791479","https://openalex.org/W2063217105","https://openalex.org/W2071709160","https://openalex.org/W2080448710","https://openalex.org/W2099707769","https://openalex.org/W2100495367","https://openalex.org/W2101234009","https://openalex.org/W2105577021","https://openalex.org/W2112796928","https://openalex.org/W2130325614","https://openalex.org/W2138190368","https://openalex.org/W2138857742","https://openalex.org/W2152175008","https://openalex.org/W2152548630","https://openalex.org/W2152942156","https://openalex.org/W2160537405","https://openalex.org/W2161113826","https://openalex.org/W2162747531","https://openalex.org/W2163922914","https://openalex.org/W2165720259","https://openalex.org/W2200277099","https://openalex.org/W2289526789","https://openalex.org/W2467126091","https://openalex.org/W2606321545","https://openalex.org/W2997591727","https://openalex.org/W4285719527","https://openalex.org/W6611025721","https://openalex.org/W6719891271"],"related_works":["https://openalex.org/W1604956872","https://openalex.org/W2950353870","https://openalex.org/W4300624757","https://openalex.org/W2602371967","https://openalex.org/W1964155876","https://openalex.org/W2153583660","https://openalex.org/W1978070069","https://openalex.org/W4378228340","https://openalex.org/W1975020933","https://openalex.org/W2009689660"],"abstract_inverted_index":{"The":[0],"choice":[1],"of":[2,8,155],"features":[3,90],"greatly":[4],"influences":[5],"the":[6,54,101,128,167],"performance":[7],"a":[9,49,72,109,112,153],"tissue":[10,169],"classification":[11,103,143,170],"system.":[12],"Despite":[13],"this,":[14],"many":[15,58],"systems":[16],"are":[17,25,66,69,92,123,194],"built":[18],"with":[19,71,137],"standard,":[20],"predefined":[21],"filter":[22,44],"banks":[23,45],"that":[24,29,91,122,180,193],"not":[26,87],"optimized":[27,195],"for":[28,94,126,132,140,165,196],"particular":[30],"application.":[31],"Representation":[32],"learning":[33,61,74,114,139,156,182],"methods":[34],"such":[35],"as":[36],"restricted":[37,63,104],"Boltzmann":[38,64,105],"machines":[39,65],"may":[40],"outperform":[41],"these":[42],"standard":[43],"because":[46],"they":[47],"learn":[48,80,120,191],"feature":[50,138,188],"description":[51],"directly":[52],"from":[53,82],"training":[55,129],"data.":[56],"Like":[57],"other":[59],"representation":[60],"methods,":[62],"unsupervised":[67,187],"and":[68,111,131,144],"trained":[70],"generative":[73,110,162],"objective;":[75],"this":[76,97],"allows":[77,117],"them":[78],"to":[79,119,174,190],"representations":[81],"unlabeled":[83],"data,":[84],"but":[85],"does":[86],"necessarily":[88],"produce":[89],"optimal":[93],"classification.":[95,133,197],"In":[96,150],"paper":[98],"we":[99],"propose":[100],"convolutional":[102],"machine,":[106],"which":[107],"combines":[108],"discriminative":[113,160,181],"objective.":[115],"This":[116,178],"it":[118],"filters":[121,192],"good":[124],"both":[125,151],"describing":[127],"data":[130],"We":[134],"present":[135],"experiments":[136],"lung":[141,168],"texture":[142],"airway":[145],"detection":[146],"in":[147],"CT":[148],"images.":[149],"applications,":[152],"combination":[154],"objectives":[157],"outperformed":[158],"purely":[159],"or":[161],"learning,":[163],"increasing,":[164],"instance,":[166],"accuracy":[171],"by":[172],"1":[173],"8":[175],"percentage":[176],"points.":[177],"shows":[179],"can":[183],"help":[184],"an":[185],"otherwise":[186],"learner":[189]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":17},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":31},{"year":2018,"cited_by_count":14},{"year":2017,"cited_by_count":16},{"year":2016,"cited_by_count":6}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2016-06-24T00:00:00"}
