{"id":"https://openalex.org/W7126027480","doi":"https://doi.org/10.1109/bibm66473.2025.11356673","title":"SC-UMamba: A Unified Architecture for Colorectal Polyp Segmentation and Classification","display_name":"SC-UMamba: A Unified Architecture for Colorectal Polyp Segmentation and Classification","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126027480","doi":"https://doi.org/10.1109/bibm66473.2025.11356673"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356673","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356673","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":"conference-paper","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/A5124288144","display_name":"Rui Li","orcid":null},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Li","raw_affiliation_strings":["Information Engineering College, Capital Normal University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Engineering College, Capital Normal University,Beijing,China","institution_ids":["https://openalex.org/I96852419"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009102045","display_name":"Dehui Qiu","orcid":"https://orcid.org/0000-0002-0085-6832"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dehui Qiu","raw_affiliation_strings":["Information Engineering College, Capital Normal University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Engineering College, Capital Normal University,Beijing,China","institution_ids":["https://openalex.org/I96852419"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003524720","display_name":"Boxuan Zhao","orcid":"https://orcid.org/0000-0002-3729-4511"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Boxuan Zhao","raw_affiliation_strings":["Information Engineering College, Capital Normal University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Engineering College, Capital Normal University,Beijing,China","institution_ids":["https://openalex.org/I96852419"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I96852419"],"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":"2435","last_page":"2440"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9417999982833862,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9417999982833862,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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":0.018400000408291817,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.011300000362098217,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7491000294685364},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5389999747276306},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5339000225067139},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.4645000100135803},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.462799996137619},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.44589999318122864},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43050000071525574},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.41690000891685486}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.767300009727478},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.762499988079071},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7491000294685364},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5389999747276306},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5339000225067139},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.4645000100135803},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.462799996137619},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.44589999318122864},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43050000071525574},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4162999987602234},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4043999910354614},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.37310001254081726},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3522999882698059},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.29980000853538513},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2700999975204468},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.2637999951839447}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356673","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356673","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":[{"score":0.5099458694458008,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G2008515350","display_name":null,"funder_award_id":"61932018,62072441,32241027,62472034","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7408022011","display_name":null,"funder_award_id":"QY24308","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2884436604","https://openalex.org/W2888358068","https://openalex.org/W3015788359","https://openalex.org/W3046240927","https://openalex.org/W3141797743","https://openalex.org/W3168236164","https://openalex.org/W3202285299","https://openalex.org/W3203480968","https://openalex.org/W4213418123","https://openalex.org/W4308456711","https://openalex.org/W4375928970","https://openalex.org/W4390669810","https://openalex.org/W4390946922","https://openalex.org/W4403062842","https://openalex.org/W4403152483"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"segmentation":[1,61,85,143],"and":[2,13,27,62,75,78,91,127,145,163],"classification":[3,63,146],"of":[4,15,153],"colorectal":[5,16],"polyps":[6],"are":[7,97],"critical":[8],"for":[9,34],"the":[10,72,84,107,125,151],"early":[11],"diagnosis":[12],"prevention":[14],"cancer.":[17],"However,":[18],"high":[19],"variability":[20],"in":[21,141],"polyp":[22],"appearance,":[23],"along":[24],"with":[25,53],"object-background":[26],"inter-class":[28],"visual":[29],"similarities,":[30],"presents":[31],"significant":[32],"challenges":[33],"automated":[35],"analysis.":[36],"In":[37],"this":[38],"paper,":[39],"we":[40],"propose":[41],"SC-UMamba,":[42],"a":[43,50,54,92],"unified":[44],"deep":[45],"learning":[46,95,102],"architecture":[47,57,108],"that":[48,133],"integrates":[49],"CNN":[51,76],"backbone":[52,120],"Mamba-based":[55],"UNet-like":[56],"to":[58,99,112,114,155],"jointly":[59],"address":[60],"tasks.":[64,105],"Specifically,":[65],"SC-UMamba":[66,134,154],"leverages":[67],"shared":[68],"feature":[69],"representations":[70],"between":[71],"SSM":[73],"encoder":[74],"backbone,":[77],"further":[79],"incorporates":[80],"semantic":[81],"guidance":[82],"from":[83],"mask":[86],"output.":[87],"Additionally,":[88],"pretrained":[89],"weights":[90],"step-based":[93],"curriculum":[94],"strategy":[96],"adopted":[98],"enable":[100],"robust":[101],"across":[103],"both":[104,142],"Furthermore,":[106],"offers":[109],"modular":[110],"flexibility":[111],"adapt":[113],"different":[115],"clinical":[116,130,166],"requirements":[117],"by":[118],"supporting":[119],"substitution.":[121],"Extensive":[122],"experiments":[123],"on":[124],"SUN-SEG":[126],"our":[128],"proprietary":[129],"dataset":[131],"demonstrate":[132],"achieves":[135],"state-of-the-art":[136],"performance,":[137],"outperforming":[138],"existing":[139],"methods":[140],"accuracy":[144],"robustness.":[147],"These":[148],"results":[149],"highlight":[150],"potential":[152],"enhance":[156],"real-time,":[157],"intelligent":[158],"assistance":[159],"during":[160],"colonoscopy":[161],"procedures":[162],"support":[164],"reliable":[165],"decision-making.":[167]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-30T00:00:00"}
