{"id":"https://openalex.org/W7124160244","doi":"https://doi.org/10.1145/3777577.3777657","title":"MCR-UNet: Towards Accurate Pulmonary Nodule Segmentation with Lightweight M-Mamba and Dual-Attention Cross-Fusion","display_name":"MCR-UNet: Towards Accurate Pulmonary Nodule Segmentation with Lightweight M-Mamba and Dual-Attention Cross-Fusion","publication_year":2025,"publication_date":"2025-10-24","ids":{"openalex":"https://openalex.org/W7124160244","doi":"https://doi.org/10.1145/3777577.3777657"},"language":null,"primary_location":{"id":"doi:10.1145/3777577.3777657","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777577.3777657","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 6th International Symposium on Artificial Intelligence for Medical Sciences","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3777577.3777657","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123018426","display_name":"Yuan Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Tian","raw_affiliation_strings":["Shenyang Jianzhu University, Shenyang, Liaoning, China"],"raw_orcid":"https://orcid.org/0009-0008-0155-6592","affiliations":[{"raw_affiliation_string":"Shenyang Jianzhu University, Shenyang, Liaoning, China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123051463","display_name":"Chen Sha","orcid":null},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Sha","raw_affiliation_strings":["Shenyang Jianzhu University, Shenyang, Liaoning, China"],"raw_orcid":"https://orcid.org/0009-0002-8306-3228","affiliations":[{"raw_affiliation_string":"Shenyang Jianzhu University, Shenyang, Liaoning, China","institution_ids":["https://openalex.org/I83714178"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I83714178"],"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":"486","last_page":"491"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9279000163078308,"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":0.9279000163078308,"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":0.021900000050663948,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.007899999618530273,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6869999766349792},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6765000224113464},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6728000044822693},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5467000007629395},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5418000221252441},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.47940000891685486},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.459199994802475},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44600000977516174}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.732699990272522},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7253000140190125},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6869999766349792},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6765000224113464},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6728000044822693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5467000007629395},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5418000221252441},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4860000014305115},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.47940000891685486},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.459199994802475},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44600000977516174},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4284999966621399},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.40139999985694885},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.3515999913215637},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C2776731575","wikidata":"https://www.wikidata.org/wiki/Q2916245","display_name":"Nodule (geology)","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.27799999713897705},{"id":"https://openalex.org/C2776256026","wikidata":"https://www.wikidata.org/wiki/Q47912","display_name":"Lung cancer","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3777577.3777657","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777577.3777657","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 6th International Symposium on Artificial Intelligence for Medical Sciences","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3777577.3777657","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777577.3777657","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 6th International Symposium on Artificial Intelligence for Medical Sciences","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6563915610313416,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2147800946","https://openalex.org/W2884436604","https://openalex.org/W3015788359","https://openalex.org/W3170841864","https://openalex.org/W4214493665","https://openalex.org/W4214709605","https://openalex.org/W4324093429","https://openalex.org/W4386782598","https://openalex.org/W4406887898","https://openalex.org/W4414243662"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"early-stage":[1],"lung":[2,71,185],"cancer":[3,186],"diagnosis":[4],"relies":[5],"on":[6,148],"effective":[7],"segmentation":[8,145],"of":[9,18,83,105,128,161],"pulmonary":[10],"nodules.":[11],"However,":[12],"traditional":[13],"methods":[14],"face":[15],"dual":[16],"challenges":[17],"deep":[19,97],"feature":[20,39],"loss":[21],"and":[22,37,62,69,99,125,166],"edge":[23,130],"blurring":[24],"in":[25,33,81,88,96,140,163,168,173],"complex":[26,174],"lesions.":[27],"To":[28],"overcome":[29],"existing":[30],"models'":[31],"limitations":[32],"modeling":[34],"long-range":[35,122],"dependencies":[36],"cross-level":[38,138],"fusion,":[40],"This":[41,177],"paper":[42],"proposes":[43],"an":[44,106],"enhanced":[45,107],"convolutional":[46,86],"neural":[47],"network,":[48],"MCR-UNet,":[49],"integrating":[50],"a":[51,56,63,154,180],"dual-attention":[52,133],"cross-fusion":[53],"(SE-CBAM)":[54],"module,":[55],"lightweight":[57],"state":[58],"space":[59],"model":[60,101,178],"(M-Mamba),":[61],"residual":[64,79],"architecture":[65],"to":[66,143],"achieve":[67],"efficient":[68],"precise":[70],"nodule":[72],"segmentation.":[73],"The":[74,103],"model's":[75],"design":[76],"incorporates":[77],"ResNet's":[78],"blocks":[80,87],"place":[82],"the":[84,89,111,126,149],"standard":[85],"U-Net":[90],"architecture,":[91],"effectively":[92],"mitigating":[93],"gradient":[94],"vanishing":[95],"networks":[98],"accelerating":[100],"convergence.":[102],"integration":[104],"M-Mamba":[108],"layer":[109],"within":[110],"encoder-decoder":[112],"achieves":[113],"two":[114],"key":[115],"objectives:":[116],"capturing":[117],"multi-scale":[118],"receptive":[119],"fields":[120],"via":[121],"dependency":[123],"modeling,":[124],"preservation":[127],"high-frequency":[129],"details.":[131],"A":[132],"cross-integration":[134],"module":[135],"dynamically":[136],"calibrates":[137],"features":[139],"skip":[141],"connections":[142],"suppress":[144],"bias.":[146],"Evaluated":[147],"LIDC-IDRI":[150],"dataset,":[151],"MCR-UNet":[152],"shows":[153],"marked":[155],"improvement":[156],"over":[157],"U-Net,":[158],"with":[159,189],"increases":[160],"2.85%":[162],"Dice":[164],"(0.9479)":[165],"4.14%":[167],"IoU":[169],"(0.9060)\u2014while":[170],"exhibiting":[171],"robustness":[172],"lesion":[175],"scenarios.":[176],"provides":[179],"reliable":[181],"tool":[182],"for":[183],"intelligent":[184],"diagnosis.":[187],"Translated":[188],"DeepL.com":[190],"(free":[191],"version)":[192]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-15T00:00:00"}
