{"id":"https://openalex.org/W7225834733","doi":"https://doi.org/10.3389/fmed.2026.2034134","title":"FMC-Net: an anatomy-aware multi-cue lightweight network for fracture classification in radiographs","display_name":"FMC-Net: an anatomy-aware multi-cue lightweight network for fracture classification in radiographs","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225834733","doi":"https://doi.org/10.3389/fmed.2026.2034134"},"language":"en","primary_location":{"id":"doi:10.3389/fmed.2026.2034134","doi":"https://doi.org/10.3389/fmed.2026.2034134","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fmed.2026.2034134","pdf_url":"https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.2034134/pdf","source":{"id":"https://openalex.org/S2597052008","display_name":"Frontiers in Medicine","issn_l":"2296-858X","issn":["2296-858X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Medicine","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.2034134/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161427764","display_name":"Chaofan Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]},{"id":"https://openalex.org/I4210127567","display_name":"Yancheng Third People's Hospital","ror":"https://ror.org/030cwsf88","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210127567"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaofan Li","raw_affiliation_strings":["Affiliated Hospital 6 of Nantong University, Yancheng Third People\u2019s Hospital"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Affiliated Hospital 6 of Nantong University, Yancheng Third People\u2019s Hospital","institution_ids":["https://openalex.org/I199305430","https://openalex.org/I4210127567"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161452993","display_name":"Peng Chen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]},{"id":"https://openalex.org/I4210127567","display_name":"Yancheng Third People's Hospital","ror":"https://ror.org/030cwsf88","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210127567"]},{"id":"https://openalex.org/I83519826","display_name":"Nanjing Medical University","ror":"https://ror.org/059gcgy73","country_code":"CN","type":"education","lineage":["https://openalex.org/I83519826"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Chen","raw_affiliation_strings":["Affiliated Hospital 6 of Nantong University, Yancheng Third People\u2019s Hospital","The Yancheng School of Clinical Medicine of Nanjing Medical University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Affiliated Hospital 6 of Nantong University, Yancheng Third People\u2019s Hospital","institution_ids":["https://openalex.org/I199305430","https://openalex.org/I4210127567"]},{"raw_affiliation_string":"The Yancheng School of Clinical Medicine of Nanjing Medical University","institution_ids":["https://openalex.org/I83519826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148295707","display_name":"Qiong Liu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Qiong Liu","raw_affiliation_strings":["School of Medical Imaging, Jiangsu Medical College"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Medical Imaging, Jiangsu Medical College","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5161492429","display_name":"Chen Ling","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]},{"id":"https://openalex.org/I4210127567","display_name":"Yancheng Third People's Hospital","ror":"https://ror.org/030cwsf88","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210127567"]},{"id":"https://openalex.org/I83519826","display_name":"Nanjing Medical University","ror":"https://ror.org/059gcgy73","country_code":"CN","type":"education","lineage":["https://openalex.org/I83519826"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chen Ling","raw_affiliation_strings":["Affiliated Hospital 6 of Nantong University, Yancheng Third People\u2019s Hospital","The Yancheng School of Clinical Medicine of Nanjing Medical University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Affiliated Hospital 6 of Nantong University, Yancheng Third People\u2019s Hospital","institution_ids":["https://openalex.org/I199305430","https://openalex.org/I4210127567"]},{"raw_affiliation_string":"The Yancheng School of Clinical Medicine of Nanjing Medical University","institution_ids":["https://openalex.org/I83519826"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5148295707","https://openalex.org/A5161492429"],"corresponding_institution_ids":["https://openalex.org/I199305430","https://openalex.org/I4210127567","https://openalex.org/I83519826"],"apc_list":{"value":3150,"currency":"CHF","value_usd":3801},"apc_paid":{"value":3150,"currency":"CHF","value_usd":3801},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9839000105857849,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9839000105857849,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.006399999838322401,"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/T10658","display_name":"Bone fractures and treatments","score":0.002199999988079071,"subfield":{"id":"https://openalex.org/subfields/2732","display_name":"Orthopedics and Sports Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6381999850273132},{"id":"https://openalex.org/C43369102","wikidata":"https://www.wikidata.org/wiki/Q2307625","display_name":"Fracture (geology)","level":2,"score":0.5257999897003174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4611999988555908},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.44369998574256897},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.427700012922287},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3937000036239624},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.38609999418258667},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3833000063896179},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3499999940395355},{"id":"https://openalex.org/C166955791","wikidata":"https://www.wikidata.org/wiki/Q629579","display_name":"Macro","level":2,"score":0.3443000018596649},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2928999960422516},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2766999900341034},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3389/fmed.2026.2034134","doi":"https://doi.org/10.3389/fmed.2026.2034134","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fmed.2026.2034134","pdf_url":"https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.2034134/pdf","source":{"id":"https://openalex.org/S2597052008","display_name":"Frontiers in Medicine","issn_l":"2296-858X","issn":["2296-858X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Medicine","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3389/fmed.2026.2034134","doi":"https://doi.org/10.3389/fmed.2026.2034134","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fmed.2026.2034134","pdf_url":"https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.2034134/pdf","source":{"id":"https://openalex.org/S2597052008","display_name":"Frontiers in Medicine","issn_l":"2296-858X","issn":["2296-858X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Medicine","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225834733.pdf","grobid_xml":"https://content.openalex.org/works/W7225834733.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W2592929672","https://openalex.org/W2912558669","https://openalex.org/W3048464064","https://openalex.org/W3096161501","https://openalex.org/W3105081694","https://openalex.org/W3137100711","https://openalex.org/W3188404242","https://openalex.org/W4200283750","https://openalex.org/W4223899585","https://openalex.org/W4224216771","https://openalex.org/W4281257637","https://openalex.org/W4286698131","https://openalex.org/W4303628775","https://openalex.org/W4317000415","https://openalex.org/W4367174735","https://openalex.org/W4381988468","https://openalex.org/W4390607226","https://openalex.org/W4390819339","https://openalex.org/W4392980000","https://openalex.org/W4395044173","https://openalex.org/W4399208718","https://openalex.org/W4399367414","https://openalex.org/W4399557997","https://openalex.org/W4401764359","https://openalex.org/W4403104689","https://openalex.org/W4403680224","https://openalex.org/W4407011933","https://openalex.org/W4408717527","https://openalex.org/W4408859207","https://openalex.org/W4409744593","https://openalex.org/W4412435269","https://openalex.org/W4415601946","https://openalex.org/W4415883058","https://openalex.org/W4415993106","https://openalex.org/W7124455784"],"related_works":[],"abstract_inverted_index":{"Objectives":[0],"Fracture":[1],"x-ray":[2,52,117,314],"classification":[3,53,94,163,267,310,326],"is":[4],"an":[5],"important":[6],"task":[7],"in":[8,19,165,210,221,233,332],"orthopaedic":[9],"image-aided":[10],"diagnosis.":[11],"Fracture-related":[12],"features":[13],"often":[14],"present":[15],"as":[16],"local":[17,33,103,255],"alterations":[18],"cortical":[20],"continuity,":[21],"fine":[22],"linear":[23],"structures,":[24,37,104,249],"and":[25,38,54,60,92,107,121,123,133,138,145,155,178,187,223,254,273,296,320,337],"abnormalities":[26],"near":[27],"joints,":[28,253],"requiring":[29],"effective":[30],"modelling":[31],"of":[32,170,242,311,334],"edges,":[34],"directional":[35,85,105],"bone":[36],"multi-stage":[39],"features.":[40],"This":[41],"study":[42],"aims":[43],"to":[44,96,252],"develop":[45],"a":[46,67,277,289,302],"lightweight":[47,304],"network":[48,70,306],"for":[49,71,226,308],"two-dimensional":[50,312],"fracture":[51,72,116,202,313,325],"evaluate":[55],"its":[56],"performance,":[57],"stability,":[58],"interpretability,":[59],"computational":[61,338],"efficiency.":[62,339],"Methods":[63],"We":[64],"proposed":[65],"FMC-Net,":[66],"multi-branch":[68,79,269,274],"convolutional":[69,81,87,271,305],"classification.":[73],"The":[74,190,316],"model":[75,125,146,191,281,317],"employs":[76],"edge-preserving":[77],"inputs,":[78],"residual":[80,270],"blocks,":[82,272],"boundary-preserving":[83],"downsampling,":[84],"strip":[86],"attention,":[88],"global":[89],"context":[90],"calibration,":[91],"multi-source":[93,266],"heads":[95],"jointly":[97],"utilise":[98],"shallow-layer":[99],"edge":[100],"responses,":[101],"mid-layer":[102],"textures,":[106],"high-level":[108],"semantic":[109],"information.":[110],"Experiments":[111],"were":[112,140,184,193,230],"conducted":[113],"on":[114,168,280],"three":[115,172],"datasets,":[118,327],"FracAtlas,":[119,176,211],"MFXR":[120,177],"OrthoFrac-XR,":[122,179],"the":[124,171,180,206,212,218,224,227,239,265,293,309],"was":[126,148],"compared":[127],"with":[128,258],"efficient":[129],"CNNs,":[130],"Transformer":[131],"architectures":[132],"hybrid":[134],"CNN-Transformer":[135],"models.":[136],"Training":[137],"testing":[139],"repeated":[141],"using":[142,151],"random":[143],"seeds,":[144],"performance":[147,164,198,322],"further":[149],"evaluated":[150],"Grad-CAM,":[152],"ablation":[153],"studies":[154],"efficiency":[156],"analysis.":[157],"Results":[158],"FMC-Net":[159,243,287,300],"achieved":[160],"superior":[161],"overall":[162],"separate":[166],"evaluations":[167],"each":[169],"public":[173],"datasets.":[174],"On":[175],"macro":[181],"F":[182],"1-scores":[183],"0.7783,":[185],"0.8141":[186],"0.8093,":[188],"respectively.":[189],"results":[192],"generally":[194],"stable,":[195],"but":[196],"category-level":[197],"varied":[199],"across":[200,247,323],"datasets:":[201],"class":[203,214,220],"recognition":[204],"remained":[205],"primary":[207],"limiting":[208],"factor":[209],"anomaly":[213],"performed":[215],"better":[216],"than":[217],"good":[219],"MFXR,":[222],"metrics":[225],"two":[228],"classes":[229],"relatively":[231],"close":[232],"OrthoFrac-XR.":[234],"Grad-CAM":[235],"visualisations":[236],"show":[237],"that":[238,264,286],"response":[240,336],"regions":[241,256],"are":[244],"primarily":[245],"distributed":[246],"bony":[248],"areas":[250],"adjacent":[251],"associated":[257],"suspected":[259],"anomalies.":[260],"Ablation":[261],"experiments":[262],"indicate":[263],"head,":[268],"downsampling":[275],"have":[276],"significant":[278],"impact":[279],"performance.":[282],"Efficiency":[283],"analysis":[284],"reveals":[285],"maintains":[288],"reasonable":[290],"balance":[291],"between":[292],"params,":[294],"FLOPs,":[295],"inference":[297],"speed.":[298],"Conclusions":[299],"provides":[301],"task-adaptive,":[303],"solution":[307],"images.":[315],"demonstrates":[318],"stable":[319],"competitive":[321],"various":[324],"whilst":[328],"offering":[329],"corresponding":[330],"advantages":[331],"terms":[333],"spatial":[335]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}
