{"id":"https://openalex.org/W4200516743","doi":"https://doi.org/10.1109/embc46164.2021.9631021","title":"Deformable Dilated Faster R-CNN for Universal Lesion Detection in CT Images","display_name":"Deformable Dilated Faster R-CNN for Universal Lesion Detection in CT Images","publication_year":2021,"publication_date":"2021-11-01","ids":{"openalex":"https://openalex.org/W4200516743","doi":"https://doi.org/10.1109/embc46164.2021.9631021","pmid":"https://pubmed.ncbi.nlm.nih.gov/34891852"},"language":"en","primary_location":{"id":"doi:10.1109/embc46164.2021.9631021","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc46164.2021.9631021","pdf_url":null,"source":{"id":"https://openalex.org/S4363607750","display_name":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","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":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://zenodo.org/record/7116972","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Fabio Hellmann","orcid":null},"institutions":[{"id":"https://openalex.org/I179225836","display_name":"University of Augsburg","ror":"https://ror.org/03p14d497","country_code":"DE","type":"education","lineage":["https://openalex.org/I179225836"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Fabio Hellmann","raw_affiliation_strings":["Chair of Human - Centered AI, University of Augsburg, Augsburg, Germany"],"affiliations":[{"raw_affiliation_string":"Chair of Human - Centered AI, University of Augsburg, Augsburg, Germany","institution_ids":["https://openalex.org/I179225836"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhao Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I179225836","display_name":"University of Augsburg","ror":"https://ror.org/03p14d497","country_code":"DE","type":"education","lineage":["https://openalex.org/I179225836"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Zhao Ren","raw_affiliation_strings":["Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany"],"affiliations":[{"raw_affiliation_string":"Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany","institution_ids":["https://openalex.org/I179225836"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Elisabeth Andre","orcid":null},"institutions":[{"id":"https://openalex.org/I179225836","display_name":"University of Augsburg","ror":"https://ror.org/03p14d497","country_code":"DE","type":"education","lineage":["https://openalex.org/I179225836"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Elisabeth Andre","raw_affiliation_strings":["Chair of Human - Centered AI, University of Augsburg, Augsburg, Germany"],"affiliations":[{"raw_affiliation_string":"Chair of Human - Centered AI, University of Augsburg, Augsburg, Germany","institution_ids":["https://openalex.org/I179225836"]}]},{"author_position":"last","author":{"id":null,"display_name":"Bjorn W. Schuller","orcid":null},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]},{"id":"https://openalex.org/I179225836","display_name":"University of Augsburg","ror":"https://ror.org/03p14d497","country_code":"DE","type":"education","lineage":["https://openalex.org/I179225836"]}],"countries":["DE","GB"],"is_corresponding":false,"raw_author_name":"Bjorn W. Schuller","raw_affiliation_strings":["Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany","Glam \u2013 the Group on Language, Audio, & Music, Imperial College London, London, UK"],"affiliations":[{"raw_affiliation_string":"Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany","institution_ids":["https://openalex.org/I179225836"]},{"raw_affiliation_string":"Glam \u2013 the Group on Language, Audio, & Music, Imperial College London, London, UK","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I179225836"],"apc_list":null,"apc_paid":null,"fwci":0.2628,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.65186882,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"2021","issue":null,"first_page":"2896","last_page":"2902"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.492900013923645,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.492900013923645,"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/T10862","display_name":"AI in cancer detection","score":0.11069999635219574,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.0502999983727932,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.7290999889373779},{"id":"https://openalex.org/keywords/dilation","display_name":"Dilation (metric space)","score":0.611299991607666},{"id":"https://openalex.org/keywords/region-of-interest","display_name":"Region of interest","score":0.5892999768257141},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.5669000148773193},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4948999881744385},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4812999963760376},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48080000281333923},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4041000008583069}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.7290999889373779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6808000206947327},{"id":"https://openalex.org/C2780757906","wikidata":"https://www.wikidata.org/wiki/Q5276676","display_name":"Dilation (metric space)","level":2,"score":0.611299991607666},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.5892999768257141},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.5669000148773193},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5425999760627747},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4948999881744385},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4812999963760376},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48080000281333923},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4699000120162964},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4041000008583069},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.33340001106262207},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.29190000891685486},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.26350000500679016},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C2989486834","wikidata":"https://www.wikidata.org/wiki/Q3808900","display_name":"True positive rate","level":2,"score":0.2529999911785126}],"mesh":[{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":4,"locations":[{"id":"doi:10.1109/embc46164.2021.9631021","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc46164.2021.9631021","pdf_url":null,"source":{"id":"https://openalex.org/S4363607750","display_name":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","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":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","raw_type":"proceedings-article"},{"id":"pmid:34891852","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34891852","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,"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":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","raw_type":null},{"id":"pmh:oai:zenodo.org:7116972","is_oa":true,"landing_page_url":"https://zenodo.org/record/7116972","pdf_url":"https://zenodo.org/record/7116972","source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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":"","raw_type":"info:eu-repo/semantics/conferencePaper"},{"id":"pmh:oai:uni-augsburg.opus-bayern.de:91487","is_oa":false,"landing_page_url":"https://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-914873","pdf_url":null,"source":{"id":"https://openalex.org/S4306400930","display_name":"OPUS (Augsburg University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I119916105","host_organization_name":"Augsburg University","host_organization_lineage":["https://openalex.org/I119916105"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"conferenceobject"}],"best_oa_location":{"id":"pmh:oai:zenodo.org:7116972","is_oa":true,"landing_page_url":"https://zenodo.org/record/7116972","pdf_url":"https://zenodo.org/record/7116972","source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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":"","raw_type":"info:eu-repo/semantics/conferencePaper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4200516743.pdf","grobid_xml":"https://content.openalex.org/works/W4200516743.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1536729381","https://openalex.org/W1990418160","https://openalex.org/W2019607817","https://openalex.org/W2034528047","https://openalex.org/W2108598243","https://openalex.org/W2322406257","https://openalex.org/W2557728737","https://openalex.org/W2601564443","https://openalex.org/W2806070179","https://openalex.org/W2883683269","https://openalex.org/W2884584803","https://openalex.org/W2890821982","https://openalex.org/W2926837263","https://openalex.org/W2937299048","https://openalex.org/W2963663752","https://openalex.org/W2966926453","https://openalex.org/W2968342525","https://openalex.org/W2979564444","https://openalex.org/W3010887580","https://openalex.org/W3091855899","https://openalex.org/W4251033893","https://openalex.org/W6696085341","https://openalex.org/W6697974390","https://openalex.org/W6751420435","https://openalex.org/W6773354936","https://openalex.org/W6778574831","https://openalex.org/W6782632949","https://openalex.org/W6786060466"],"related_works":[],"abstract_inverted_index":{"Cancer":[0],"is":[1,26,65],"a":[2,33,38,75,80,203],"major":[3],"public":[4],"health":[5],"issue":[6],"and":[7,47,79,172,195],"takes":[8],"the":[9,30,68,103,117,126,132,140,144,147,157,177,190,196,200],"second-highest":[10],"toll":[11],"of":[12,32,52,70,82,108,152,161,168,179,192,198,208],"deaths":[13],"caused":[14],"by":[15,74],"non-communicable":[16],"diseases":[17],"worldwide.":[18],"Automatically":[19],"detecting":[20],"lesions":[21,57],"at":[22],"an":[23,112],"early":[24],"stage":[25],"essential":[27],"to":[28,55,86,97,130],"increase":[29],"chance":[31],"cure.":[34],"This":[35,187],"study":[36],"proposes":[37],"novel":[39],"dilated":[40,121],"Faster":[41,71],"R-CNN":[42],"with":[43,125,135,166],"modulated":[44,48,91,104,127,148,204],"deformable":[45,49,92,99,105,128,149,205],"convolution":[46],"positive-sensitive":[50,106,150,206],"region":[51,76,81,107,151,207],"interest":[53,83,109,153,209],"pooling":[54,84,110,154,210],"detect":[56],"in":[58,176],"computer":[59],"tomography":[60],"images.":[61],"A":[62],"pre-trained":[63],"VGG-16":[64,133],"transferred":[66],"as":[67],"backbone":[69],"R-CNN,":[72],"followed":[73],"proposal":[77],"network":[78],"layer":[85,211],"achieve":[87],"lesion":[88,214],"detection.":[89],"The":[90],"convolutional":[93,100],"layers":[94],"are":[95,123],"employed":[96],"learn":[98],"filters,":[101],"while":[102],"provides":[111],"enhanced":[113],"feature":[114,118],"extraction":[115],"on":[116,143,164],"maps.":[119],"Moreover,":[120],"convolutions":[122,129],"combined":[124],"fine-tune":[131],"model":[134,155],"multi-scale":[136],"receptive":[137],"fields.":[138],"In":[139],"experiments":[141],"evaluated":[142],"DeepLesion":[145],"dataset,":[146],"achieves":[156],"highest":[158],"sensitivity":[159],"score":[160],"58.8":[162],"%":[163],"average":[165,182],"dilation":[167,193],"[4,":[169],"4,":[170],"4]":[171],"outperforms":[173],"state-of-the-art":[174],"models":[175],"range":[178],"[2],":[180],"[8]":[181],"false":[183],"positives":[184],"per":[185],"image.":[186],"research":[188],"demonstrates":[189],"suitability":[191],"modifications":[194],"possibility":[197],"enhancing":[199],"performance":[201],"using":[202],"for":[212],"universal":[213],"detectors.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2021-12-31T00:00:00"}