{"id":"https://openalex.org/W6929540693","doi":"https://doi.org/10.48550/arxiv.2507.09731","title":"Pre-trained Under Noise: A Framework for Robust Bone Fracture Detection in Medical Imaging","display_name":"Pre-trained Under Noise: A Framework for Robust Bone Fracture Detection in Medical Imaging","publication_year":2025,"publication_date":"2025-07-13","ids":{"openalex":"https://openalex.org/W6929540693","doi":"https://doi.org/10.48550/arxiv.2507.09731"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2507.09731","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.09731","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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2507.09731","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Hoover, Robby","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hoover, Robby","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Elsayed, Nelly","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elsayed, Nelly","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"ElSayed, Zag","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"ElSayed, Zag","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Li, Chengcheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Chengcheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11150","display_name":"Endoplasmic Reticulum Stress and Disease","score":0.14309999346733093,"subfield":{"id":"https://openalex.org/subfields/1307","display_name":"Cell Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11150","display_name":"Endoplasmic Reticulum Stress and Disease","score":0.14309999346733093,"subfield":{"id":"https://openalex.org/subfields/1307","display_name":"Cell Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10613","display_name":"Virus-based gene therapy research","score":0.08299999684095383,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10137","display_name":"Multiple Sclerosis Research Studies","score":0.0414000004529953,"subfield":{"id":"https://openalex.org/subfields/2734","display_name":"Pathology and Forensic Medicine"},"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/robustness","display_name":"Robustness (evolution)","score":0.6972000002861023},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5931000113487244},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.5501999855041504},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4876999855041504},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4429999887943268},{"id":"https://openalex.org/keywords/replicate","display_name":"Replicate","score":0.41819998621940613},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.40610000491142273}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6972000002861023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6496999859809875},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6424000263214111},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5931000113487244},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.5501999855041504},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4876999855041504},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46149998903274536},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44780001044273376},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4429999887943268},{"id":"https://openalex.org/C2781162219","wikidata":"https://www.wikidata.org/wiki/Q26250693","display_name":"Replicate","level":2,"score":0.41819998621940613},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.40610000491142273},{"id":"https://openalex.org/C2776441800","wikidata":"https://www.wikidata.org/wiki/Q68833","display_name":"Bone fracture","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C2988170871","wikidata":"https://www.wikidata.org/wiki/Q11000047","display_name":"Healthcare system","level":3,"score":0.2653000056743622},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26260000467300415},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2624000012874603},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C3017601658","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image enhancement","level":3,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2507.09731","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.09731","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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2507.09731","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.09731","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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Medical":[0],"Imagings":[1],"are":[2,63,69],"considered":[3],"one":[4],"of":[5,22,44,118],"the":[6,20,42,64,93,97,103,112,116,119,137],"crucial":[7],"diagnostic":[8],"tools":[9],"for":[10,27,146],"different":[11,168,181],"bones-related":[12],"diseases,":[13],"especially":[14],"bones":[15],"fractures.":[16],"This":[17,87,122],"paper":[18,88,123,141],"investigates":[19],"robustness":[21],"pre-trained":[23,66,104,169],"deep":[24,47,170],"learning":[25,48,153,171],"models":[26,49,72,105,175],"classifying":[28],"bone":[29,77,98],"fractures":[30,99],"in":[31,180],"X-ray":[32,120],"images":[33,81],"and":[34,61,101,154,166],"seeks":[35],"to":[36,75,111,125],"address":[37],"global":[38],"healthcare":[39],"disparity":[40],"through":[41],"lens":[43],"technology.":[45],"Three":[46],"have":[50],"been":[51],"tested":[52],"under":[53],"varying":[54],"simulated":[55],"equipment":[56],"quality":[57,117],"conditions.":[58],"ResNet50,":[59],"VGG16":[60],"EfficientNetv2":[62],"three":[65],"architectures":[67],"which":[68],"compared.":[70],"These":[71],"were":[73,82],"used":[74,179],"perform":[76],"fracture":[78],"classification":[79],"as":[80],"progressively":[83],"degraded":[84],"using":[85,151],"noise.":[86],"specifically":[89],"empirically":[90],"studies":[91],"how":[92,102,164],"noise":[94,113,156],"can":[95,107,176],"affect":[96,115],"detection":[100],"performance":[106],"be":[108,177],"changes":[109],"due":[110],"that":[114],"images.":[121],"aims":[124],"help":[126],"replicate":[127],"real":[128],"world":[129],"challenges":[130],"experienced":[131],"by":[132],"medical":[133],"imaging":[134],"technicians":[135],"across":[136],"world.":[138],"Thus,":[139],"this":[140],"establishes":[142],"a":[143],"methodological":[144],"framework":[145],"assessing":[147],"AI":[148],"model":[149],"degradation":[150],"transfer":[152],"controlled":[155],"augmentation.":[157],"The":[158],"findings":[159],"provide":[160],"practical":[161],"insight":[162],"into":[163],"robust":[165],"generalizable":[167],"powered":[172],"computer":[173],"vision":[174],"when":[178],"contexts.":[182]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}