{"id":"https://openalex.org/W4402727107","doi":"https://doi.org/10.1109/mwscas60917.2024.10658719","title":"MedSegNet: A Lightweight Convolutional Network Combining Dual Self-Attention and Multi-Scale Attention for Medical Image Segmentation","display_name":"MedSegNet: A Lightweight Convolutional Network Combining Dual Self-Attention and Multi-Scale Attention for Medical Image Segmentation","publication_year":2024,"publication_date":"2024-08-11","ids":{"openalex":"https://openalex.org/W4402727107","doi":"https://doi.org/10.1109/mwscas60917.2024.10658719"},"language":"en","primary_location":{"id":"doi:10.1109/mwscas60917.2024.10658719","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwscas60917.2024.10658719","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS)","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/A5082675058","display_name":"Subrato Bharati","orcid":"https://orcid.org/0000-0001-8849-4313"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Subrato Bharati","raw_affiliation_strings":["Concordia University,Department of Electrical and Computer Engineering,Montreal,QC,Canada,H3G 1M8"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Concordia University,Department of Electrical and Computer Engineering,Montreal,QC,Canada,H3G 1M8","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026732611","display_name":"M. Omair Ahmad","orcid":"https://orcid.org/0000-0002-1427-1237"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"M. Omair Ahmad","raw_affiliation_strings":["Concordia University,Department of Electrical and Computer Engineering,Montreal,QC,Canada,H3G 1M8"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Concordia University,Department of Electrical and Computer Engineering,Montreal,QC,Canada,H3G 1M8","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013967994","display_name":"M.N.S. Swamy","orcid":"https://orcid.org/0000-0002-3989-5476"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"M.N.S. Swamy","raw_affiliation_strings":["Concordia University,Department of Electrical and Computer Engineering,Montreal,QC,Canada,H3G 1M8"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Concordia University,Department of Electrical and Computer Engineering,Montreal,QC,Canada,H3G 1M8","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"965","last_page":"969"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9994999766349792,"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":0.9993000030517578,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7729511857032776},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.7326482534408569},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5608037710189819},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5553002953529358},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.541948676109314},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5410266518592834},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4645858108997345},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07601213455200195}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7729511857032776},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.7326482534408569},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5608037710189819},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5553002953529358},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.541948676109314},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5410266518592834},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4645858108997345},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07601213455200195},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mwscas60917.2024.10658719","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwscas60917.2024.10658719","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2167510172","https://openalex.org/W2752782242","https://openalex.org/W2798122215","https://openalex.org/W2888358068","https://openalex.org/W2891511539","https://openalex.org/W2902329146","https://openalex.org/W2963284331","https://openalex.org/W2963529609","https://openalex.org/W2980185997","https://openalex.org/W2986785750","https://openalex.org/W2988226917","https://openalex.org/W2996290406","https://openalex.org/W3020387338","https://openalex.org/W3024640380","https://openalex.org/W3027763298","https://openalex.org/W3060373687","https://openalex.org/W3092344722","https://openalex.org/W3104061658","https://openalex.org/W3163640608","https://openalex.org/W3204269019","https://openalex.org/W3204497690","https://openalex.org/W4283828743","https://openalex.org/W4293084457","https://openalex.org/W4312568229","https://openalex.org/W4387197204","https://openalex.org/W4390190475","https://openalex.org/W4400230716","https://openalex.org/W6684372118"],"related_works":["https://openalex.org/W2317351040","https://openalex.org/W4379231730","https://openalex.org/W2952466936","https://openalex.org/W4389858081","https://openalex.org/W1988622314","https://openalex.org/W2393949104","https://openalex.org/W3046201198","https://openalex.org/W4293061921","https://openalex.org/W2501551404","https://openalex.org/W1522196789"],"abstract_inverted_index":{"In":[0,95],"this":[1],"work,":[2],"we":[3],"propose":[4],"a":[5,19,59,67,138],"novel":[6],"lightweight":[7],"convolutional":[8],"neural":[9],"network":[10,48],"called":[11],"MedSegNet":[12,79,87,101,135],"that":[13,71],"innovatively":[14],"incorporates":[15],"residual":[16],"modules":[17],"with":[18,66,98],"fusion":[20],"of":[21,32,36,58,70,109,134],"dual":[22],"self-attention":[23],"and":[24,44,84,114,121],"multi-scale":[25],"attention":[26],"mechanisms,":[27],"designed":[28],"for":[29,56,141],"the":[30,85,91,132],"segmentation":[31,54,110,144],"three":[33,81],"different":[34,82],"types":[35],"medical":[37,126,142],"images":[38,57],"such":[39],"as":[40],"CT,":[41],"non-mydriatic":[42],"3CCD,":[43],"colonoscopy":[45],"images.":[46],"This":[47],"has":[49,102],"demonstrated":[50],"proficiency":[51],"in":[52,107,125],"executing":[53],"tasks":[55],"specific":[60],"modality,":[61],"depending":[62],"upon":[63],"adequate":[64],"training":[65],"representative":[68],"dataset":[69],"same":[72],"modality.":[73],"Experiments":[74],"are":[75],"implemented":[76],"to":[77,105,123,136],"train":[78],"using":[80],"datasets,":[83],"trained":[86],"is":[88],"tested":[89],"on":[90],"respective":[92],"test":[93],"datasets.":[94],"comparative":[96],"analyses":[97],"state-of-the-art":[99],"models,":[100],"been":[103],"shown":[104],"outperform":[106],"terms":[108],"dice":[111],"coefficient":[112],"(DSC)":[113],"intersection":[115],"over":[116],"union":[117],"(IoU),":[118],"computational":[119],"efficiency,":[120],"robustness":[122],"variations":[124],"imaging":[127],"modalities.":[128],"These":[129],"results":[130],"highlight":[131],"potential":[133],"set":[137],"new":[139],"benchmark":[140],"image":[143],"tasks.":[145]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
