{"id":"https://openalex.org/W7126109536","doi":"https://doi.org/10.1109/bibm66473.2025.11356594","title":"MMMNet: Multimodal Feature Fusion and Multilevel Representation Merging for Pulmonary Nodule Classification","display_name":"MMMNet: Multimodal Feature Fusion and Multilevel Representation Merging for Pulmonary Nodule Classification","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126109536","doi":"https://doi.org/10.1109/bibm66473.2025.11356594"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356594","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356594","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015793756","display_name":"Haihua Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123940","display_name":"Ministry of Education","ror":"https://ror.org/02xv42m49","country_code":"PT","type":"government","lineage":["https://openalex.org/I4210117458","https://openalex.org/I4210123940"]},{"id":"https://openalex.org/I4210141165","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64","country_code":"TW","type":"government","lineage":["https://openalex.org/I4210141165","https://openalex.org/I4405257903"]}],"countries":["PT","TW"],"is_corresponding":true,"raw_author_name":"Haihua Huang","raw_affiliation_strings":["Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education,Shanghai,China","institution_ids":["https://openalex.org/I4210123940","https://openalex.org/I4210141165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124228013","display_name":"Fan Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123940","display_name":"Ministry of Education","ror":"https://ror.org/02xv42m49","country_code":"PT","type":"government","lineage":["https://openalex.org/I4210117458","https://openalex.org/I4210123940"]},{"id":"https://openalex.org/I4210141165","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64","country_code":"TW","type":"government","lineage":["https://openalex.org/I4210141165","https://openalex.org/I4405257903"]}],"countries":["PT","TW"],"is_corresponding":false,"raw_author_name":"Fan Yang","raw_affiliation_strings":["Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education,Shanghai,China","institution_ids":["https://openalex.org/I4210123940","https://openalex.org/I4210141165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028007364","display_name":"Dewen Tang","orcid":"https://orcid.org/0009-0004-9813-9760"},"institutions":[{"id":"https://openalex.org/I4210099446","display_name":"Huadong Hospital","ror":"https://ror.org/012wm7481","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210099446"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongfang Tang","raw_affiliation_strings":["Huadong Hospital Affiliated to Fudan University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Huadong Hospital Affiliated to Fudan University,Shanghai,China","institution_ids":["https://openalex.org/I4210099446"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124288057","display_name":"Ting Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123940","display_name":"Ministry of Education","ror":"https://ror.org/02xv42m49","country_code":"PT","type":"government","lineage":["https://openalex.org/I4210117458","https://openalex.org/I4210123940"]},{"id":"https://openalex.org/I4210141165","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64","country_code":"TW","type":"government","lineage":["https://openalex.org/I4210141165","https://openalex.org/I4405257903"]}],"countries":["PT","TW"],"is_corresponding":false,"raw_author_name":"Ting Xiao","raw_affiliation_strings":["Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education,Shanghai,China","institution_ids":["https://openalex.org/I4210123940","https://openalex.org/I4210141165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022550580","display_name":"Hai Yang","orcid":"https://orcid.org/0000-0002-1161-4337"},"institutions":[{"id":"https://openalex.org/I4210123940","display_name":"Ministry of Education","ror":"https://ror.org/02xv42m49","country_code":"PT","type":"government","lineage":["https://openalex.org/I4210117458","https://openalex.org/I4210123940"]},{"id":"https://openalex.org/I4210141165","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64","country_code":"TW","type":"government","lineage":["https://openalex.org/I4210141165","https://openalex.org/I4405257903"]}],"countries":["PT","TW"],"is_corresponding":false,"raw_author_name":"Hai Yang","raw_affiliation_strings":["Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education,Shanghai,China","institution_ids":["https://openalex.org/I4210123940","https://openalex.org/I4210141165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124205637","display_name":"Zhe Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123940","display_name":"Ministry of Education","ror":"https://ror.org/02xv42m49","country_code":"PT","type":"government","lineage":["https://openalex.org/I4210117458","https://openalex.org/I4210123940"]},{"id":"https://openalex.org/I4210141165","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64","country_code":"TW","type":"government","lineage":["https://openalex.org/I4210141165","https://openalex.org/I4405257903"]}],"countries":["PT","TW"],"is_corresponding":false,"raw_author_name":"Zhe Wang","raw_affiliation_strings":["Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education,Shanghai,China","institution_ids":["https://openalex.org/I4210123940","https://openalex.org/I4210141165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124277212","display_name":"Wen Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099446","display_name":"Huadong Hospital","ror":"https://ror.org/012wm7481","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210099446"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Gao","raw_affiliation_strings":["Huadong Hospital Affiliated to Fudan University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Huadong Hospital Affiliated to Fudan University,Shanghai,China","institution_ids":["https://openalex.org/I4210099446"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5015793756"],"corresponding_institution_ids":["https://openalex.org/I4210123940","https://openalex.org/I4210141165"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.74731273,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5034","last_page":"5041"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.7008000016212463,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.7008000016212463,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.11699999868869781,"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/T10862","display_name":"AI in cancer detection","score":0.03849999979138374,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5874999761581421},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5491999983787537},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.42649999260902405},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41929998993873596},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.36489999294281006},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.36419999599456787},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.34220001101493835},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.34060001373291016}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7228000164031982},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6715999841690063},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5874999761581421},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5491999983787537},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.42649999260902405},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41929998993873596},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.36489999294281006},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.36419999599456787},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.34220001101493835},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.34060001373291016},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.32989999651908875},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.3093000054359436},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3043999969959259},{"id":"https://openalex.org/C2982962833","wikidata":"https://www.wikidata.org/wiki/Q17092450","display_name":"Information fusion","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.28610000014305115},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.26820001006126404},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2605000138282776},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356594","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356594","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6916841864585876}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1986649315","https://openalex.org/W2136853919","https://openalex.org/W2194775991","https://openalex.org/W2561512519","https://openalex.org/W2604463720","https://openalex.org/W2741140850","https://openalex.org/W2793409683","https://openalex.org/W2794187429","https://openalex.org/W2800369883","https://openalex.org/W2963777800","https://openalex.org/W2963945816","https://openalex.org/W2983132969","https://openalex.org/W2991039936","https://openalex.org/W3007268491","https://openalex.org/W3014881861","https://openalex.org/W3047040425","https://openalex.org/W3089876420","https://openalex.org/W3096275543","https://openalex.org/W4296232908","https://openalex.org/W4307640249","https://openalex.org/W4366729641","https://openalex.org/W4382011751","https://openalex.org/W4386304030","https://openalex.org/W4393935425","https://openalex.org/W4395663867","https://openalex.org/W4396650193","https://openalex.org/W4402515291","https://openalex.org/W4405395399"],"related_works":[],"abstract_inverted_index":{"In":[0],"early":[1,25],"lung":[2],"cancer":[3],"screening,":[4],"precise":[5],"classification":[6,85,145],"of":[7,14,166,177],"benign":[8],"and":[9,21,44,48,74,101,113,131,146,174,197,206],"malignant":[10],"pulmonary":[11,83],"nodules":[12],"is":[13,157],"critical":[15],"importance":[16],"to":[17,58,108,125,141,149],"clinical":[18,207],"decision":[19],"making":[20],"individualized":[22],"treatment.":[23],"Although":[24],"methods":[26,37],"have":[27,38],"achieved":[28],"considerable":[29],"progress,":[30],"two":[31,88],"main":[32],"problems":[33],"remain:":[34],"existing":[35],"diagnostic":[36],"limitations":[39],"in":[40,105,202],"utilizing":[41],"multimodal":[42,50,80,152,194],"data":[43],"capturing":[45],"semantic":[46,129],"information,":[47],"traditional":[49],"approaches":[51],"relying":[52],"on":[53,159],"late-stage":[54],"feature":[55,111,195],"fusion":[56],"fail":[57],"facilitate":[59],"the":[60,122,143,151,160,189,192],"valuable":[61],"information":[62,130],"from":[63],"internal":[64],"model":[65],"layers.":[66],"To":[67],"overcome":[68],"these":[69],"challenges,":[70],"we":[71],"propose":[72],"Multimodal":[73,92],"Multilevel":[75,115],"Merging":[76],"Net":[77],"(MMMNet),":[78],"a":[79,91,110,114,175,199],"architecture":[81],"for":[82],"nodule":[84],"that":[86,96,119,188],"includes":[87],"innovative":[89],"modules:":[90],"Feature":[93,116],"Fusion":[94],"Module":[95,118],"combines":[97],"computed":[98],"tomography":[99],"scans":[100],"text":[102],"annotations":[103],"parallelly":[104],"multiple":[106],"layers":[107],"construct":[109],"pyramid,":[112],"Merge":[117],"recursively":[120],"merges":[121],"fused":[123],"features":[124],"utilize":[126],"both":[127],"high-level":[128],"low-level":[132],"visual":[133],"characteristics.":[134],"The":[135,154],"approach":[136,156,190],"also":[137],"integrates":[138],"Focal":[139],"Loss":[140,148],"tackle":[142],"imbalanced":[144],"Contrastive":[147],"align":[150],"features.":[153],"proposed":[155],"evaluated":[158],"LIDC-IDRI":[161],"dataset,":[162],"yielding":[163],"an":[164],"accuracy":[165],"<tex":[167,178],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[168,179],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathbf{9":[169,180],"1.":[170],"7":[171],"3":[172],"\\%}$</tex>":[173],"specificity":[176],"5.":[181],"5":[182],"2":[183],"\\%}$</tex>.":[184],"Experiment":[185],"results":[186],"show":[187],"enhances":[191],"indepth":[193],"mining":[196],"has":[198],"promising":[200],"potential":[201],"medical":[203],"image":[204],"analysis":[205],"application.":[208]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}
