{"id":"https://openalex.org/W3214338115","doi":"https://doi.org/10.1117/12.2612660","title":"Universal lesion detection in CT scans using neural network ensembles","display_name":"Universal lesion detection in CT scans using neural network ensembles","publication_year":2022,"publication_date":"2022-04-01","ids":{"openalex":"https://openalex.org/W3214338115","doi":"https://doi.org/10.1117/12.2612660","mag":"3214338115"},"language":"en","primary_location":{"id":"doi:10.1117/12.2612660","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2612660","pdf_url":null,"source":{"id":"https://openalex.org/S4363606689","display_name":"Medical Imaging 2022: Computer-Aided Diagnosis","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":"Medical Imaging 2022: Computer-Aided Diagnosis","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2111.04886","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043152724","display_name":"Tarun Mattikalli","orcid":null},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tarun Mattikalli","raw_affiliation_strings":["National Institutes of Health  (United States)"],"affiliations":[{"raw_affiliation_string":"National Institutes of Health  (United States)","institution_ids":["https://openalex.org/I1299303238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048201234","display_name":"Tejas Sudharshan Mathai","orcid":"https://orcid.org/0009-0005-8440-1702"},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tejas Sudharshan Mathai","raw_affiliation_strings":["National Institutes of Health (United States)"],"affiliations":[{"raw_affiliation_string":"National Institutes of Health (United States)","institution_ids":["https://openalex.org/I1299303238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016047550","display_name":"Ronald M. Summers","orcid":"https://orcid.org/0000-0001-8081-7376"},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ronald M. Summers","raw_affiliation_strings":["National Institutes of Health (United States)"],"affiliations":[{"raw_affiliation_string":"National Institutes of Health (United States)","institution_ids":["https://openalex.org/I1299303238"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043152724"],"corresponding_institution_ids":["https://openalex.org/I1299303238"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00075438,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"64","last_page":"64"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"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":0.9969000220298767,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9886000156402588,"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/false-positive-paradox","display_name":"False positive paradox","score":0.7704963684082031},{"id":"https://openalex.org/keywords/lesion","display_name":"Lesion","score":0.6962623596191406},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5862188935279846},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5616130828857422},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4796314835548401},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41123947501182556},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3824321925640106},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.332179456949234},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.12469863891601562}],"concepts":[{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.7704963684082031},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.6962623596191406},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5862188935279846},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5616130828857422},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4796314835548401},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41123947501182556},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3824321925640106},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.332179456949234},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.12469863891601562}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1117/12.2612660","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2612660","pdf_url":null,"source":{"id":"https://openalex.org/S4363606689","display_name":"Medical Imaging 2022: Computer-Aided Diagnosis","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":"Medical Imaging 2022: Computer-Aided Diagnosis","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2111.04886","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.04886","pdf_url":"https://arxiv.org/pdf/2111.04886","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:3214338115","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2111.04886","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2111.04886","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2111.04886","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2111.04886","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.04886","pdf_url":"https://arxiv.org/pdf/2111.04886","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3214338115.pdf"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W2373606074","https://openalex.org/W2883683269","https://openalex.org/W2884584803","https://openalex.org/W2950141105","https://openalex.org/W2963008565","https://openalex.org/W2963351448","https://openalex.org/W2979362285","https://openalex.org/W2979564444","https://openalex.org/W3084433012","https://openalex.org/W3102701618","https://openalex.org/W3117686595","https://openalex.org/W3127743092","https://openalex.org/W3165446958","https://openalex.org/W3172087149","https://openalex.org/W4200409436","https://openalex.org/W6742348326","https://openalex.org/W6753721087","https://openalex.org/W6759142798","https://openalex.org/W6763376248","https://openalex.org/W6764322716","https://openalex.org/W6766509321","https://openalex.org/W6782632949","https://openalex.org/W6782744377","https://openalex.org/W6795805722","https://openalex.org/W6910687937"],"related_works":["https://openalex.org/W2966792622","https://openalex.org/W2951166644","https://openalex.org/W2286934576","https://openalex.org/W1975536293","https://openalex.org/W2535841211","https://openalex.org/W2761701131","https://openalex.org/W2946463443","https://openalex.org/W3155264285","https://openalex.org/W3042238984","https://openalex.org/W3201986668","https://openalex.org/W3027656794","https://openalex.org/W2980215921","https://openalex.org/W2588959707","https://openalex.org/W1990592734","https://openalex.org/W1999081932","https://openalex.org/W2030984404","https://openalex.org/W2810348568","https://openalex.org/W3046484918","https://openalex.org/W3090826341","https://openalex.org/W2772915230"],"abstract_inverted_index":{"In":[0],"clinical":[1,53,100],"practice,":[2],"radiologists":[3,45],"are":[4],"reliant":[5],"on":[6],"the":[7,27,61,74,107,136],"lesion":[8,19,143],"size":[9,38],"when":[10],"distinguishing":[11],"metastatic":[12],"from":[13],"non-metastatic":[14],"lesions.":[15],"A":[16],"prerequisite":[17],"for":[18,78,114,142],"sizing":[20,115],"is":[21],"their":[22,37],"detection,":[23],"as":[24],"it":[25],"promotes":[26],"downstream":[28],"assessment":[29],"of":[30,63,106,119,123,138],"tumor":[31],"spread.":[32],"However,":[33],"lesions":[34,49,71,113],"vary":[35],"in":[36,41,73,145],"and":[39,44,93,121],"appearance":[40],"CT":[42,147],"scans,":[43],"often":[46],"miss":[47],"small":[48],"during":[50],"a":[51,83,117],"busy":[52],"day.":[54],"To":[55],"overcome":[56],"these":[57],"challenges,":[58],"we":[59,81,102],"propose":[60],"use":[62],"state-of-the-art":[64,140],"detection":[65,95,109,144],"neural":[66],"networks":[67],"to":[68,88,98,111],"flag":[69],"suspicious":[70],"present":[72],"NIH":[75],"DeepLesion":[76],"dataset":[77],"sizing.":[79],"Additionally,":[80],"incorporate":[82],"bounding":[84],"box":[85],"fusion":[86],"technique":[87],"minimize":[89],"false":[90],"positives":[91],"(FP)":[92],"improve":[94,132],"accuracy.":[96],"Finally,":[97],"resemble":[99],"usage,":[101],"constructed":[103],"an":[104],"ensemble":[105],"best":[108],"models":[110],"localize":[112],"with":[116],"precision":[118],"65.17%":[120],"sensitivity":[122],"91.67%":[124],"at":[125],"4":[126],"FP":[127],"per":[128],"image.":[129],"Our":[130],"results":[131],"upon":[133],"or":[134],"maintain":[135],"performance":[137],"current":[139],"methods":[141],"challenging":[146],"scans.":[148]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}