{"id":"https://openalex.org/W4417269342","doi":"https://doi.org/10.1016/j.bspc.2025.109390","title":"Bias Corrected Twin Squeeze-and-Excitation Attention Enhanced UNet for brain tumor segmentation","display_name":"Bias Corrected Twin Squeeze-and-Excitation Attention Enhanced UNet for brain tumor segmentation","publication_year":2025,"publication_date":"2025-12-12","ids":{"openalex":"https://openalex.org/W4417269342","doi":"https://doi.org/10.1016/j.bspc.2025.109390"},"language":"en","primary_location":{"id":"doi:10.1016/j.bspc.2025.109390","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.bspc.2025.109390","pdf_url":null,"source":{"id":"https://openalex.org/S8427965","display_name":"Biomedical Signal Processing and Control","issn_l":"1746-8094","issn":["1746-8094","1746-8108"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Signal Processing and Control","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.bspc.2025.109390","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011529622","display_name":"Abhishek Jadhav","orcid":"https://orcid.org/0000-0002-2818-6359"},"institutions":[{"id":"https://openalex.org/I91277730","display_name":"Maulana Azad National Institute of Technology","ror":"https://ror.org/026vtd268","country_code":"IN","type":"education","lineage":["https://openalex.org/I91277730"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Abhishek Jadhav","raw_affiliation_strings":["Department of CSE, MANIT, Bhopal, 462003, M.P, India"],"raw_orcid":"https://orcid.org/0000-0002-2818-6359","affiliations":[{"raw_affiliation_string":"Department of CSE, MANIT, Bhopal, 462003, M.P, India","institution_ids":["https://openalex.org/I91277730"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101517869","display_name":"Akhtar Rasool","orcid":"https://orcid.org/0000-0002-7759-9571"},"institutions":[{"id":"https://openalex.org/I91277730","display_name":"Maulana Azad National Institute of Technology","ror":"https://ror.org/026vtd268","country_code":"IN","type":"education","lineage":["https://openalex.org/I91277730"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Akhtar Rasool","raw_affiliation_strings":["Department of CSE, MANIT, Bhopal, 462003, M.P, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of CSE, MANIT, Bhopal, 462003, M.P, India","institution_ids":["https://openalex.org/I91277730"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023852902","display_name":"Manasi Gyanchandani","orcid":"https://orcid.org/0000-0003-3127-1770"},"institutions":[{"id":"https://openalex.org/I91277730","display_name":"Maulana Azad National Institute of Technology","ror":"https://ror.org/026vtd268","country_code":"IN","type":"education","lineage":["https://openalex.org/I91277730"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manasi Gyanchandani","raw_affiliation_strings":["Department of CSE, MANIT, Bhopal, 462003, M.P, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of CSE, MANIT, Bhopal, 462003, M.P, India","institution_ids":["https://openalex.org/I91277730"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5011529622"],"corresponding_institution_ids":["https://openalex.org/I91277730"],"apc_list":{"value":2420,"currency":"USD","value_usd":2420},"apc_paid":{"value":2420,"currency":"USD","value_usd":2420},"fwci":1.8142,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.88273134,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"115","issue":null,"first_page":"109390","last_page":"109390"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6413000226020813,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6413000226020813,"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":0.2867000102996826,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.03189999982714653,"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/context","display_name":"Context (archaeology)","score":0.6355999708175659},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6270999908447266},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6114000082015991},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5637000203132629},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5368000268936157},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5138999819755554},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.46799999475479126},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.444599986076355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7846999764442444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7214999794960022},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6355999708175659},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6270999908447266},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6114000082015991},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5637000203132629},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5368000268936157},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5138999819755554},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.46799999475479126},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.444599986076355},{"id":"https://openalex.org/C2779130545","wikidata":"https://www.wikidata.org/wiki/Q233309","display_name":"Brain tumor","level":2,"score":0.3806000053882599},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.35850000381469727},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.33059999346733093},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3206999897956848},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3089999854564667},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2565000057220459}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.bspc.2025.109390","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.bspc.2025.109390","pdf_url":null,"source":{"id":"https://openalex.org/S8427965","display_name":"Biomedical Signal Processing and Control","issn_l":"1746-8094","issn":["1746-8094","1746-8108"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Signal Processing and Control","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.bspc.2025.109390","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.bspc.2025.109390","pdf_url":null,"source":{"id":"https://openalex.org/S8427965","display_name":"Biomedical Signal Processing and Control","issn_l":"1746-8094","issn":["1746-8094","1746-8108"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Signal Processing and Control","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1884191083","https://openalex.org/W1998070036","https://openalex.org/W2104095591","https://openalex.org/W2117340355","https://openalex.org/W2136573752","https://openalex.org/W2587828787","https://openalex.org/W2752782242","https://openalex.org/W2804047627","https://openalex.org/W2916213758","https://openalex.org/W3026002147","https://openalex.org/W3096087094","https://openalex.org/W3115325129","https://openalex.org/W3118974483","https://openalex.org/W3128009222","https://openalex.org/W3158095128","https://openalex.org/W3163071387","https://openalex.org/W3176427953","https://openalex.org/W4200285766","https://openalex.org/W4200462422","https://openalex.org/W4229012497","https://openalex.org/W4230920194","https://openalex.org/W4287448984","https://openalex.org/W4290928897","https://openalex.org/W4293152672","https://openalex.org/W4293652213","https://openalex.org/W4294862191","https://openalex.org/W4312443924","https://openalex.org/W4381332772","https://openalex.org/W4387807571","https://openalex.org/W4388041610","https://openalex.org/W4388079395","https://openalex.org/W4391693184","https://openalex.org/W4401157221","https://openalex.org/W4405861557","https://openalex.org/W4406610928"],"related_works":[],"abstract_inverted_index":{"Brain":[0],"tumor":[1,205,232],"segmentation":[2],"is":[3,122,149,170],"crucial":[4],"in":[5,18,37,43,157,192,200],"the":[6,31,83,94,128,174,209],"context":[7],"of":[8,33,187],"deep":[9,34,65,111],"learning-based":[10],"medical":[11,38],"image":[12,39],"analysis,":[13],"where":[14],"accurate":[15,142,229],"delineation":[16,233],"aids":[17],"detecting":[19],"abnormalities,":[20],"treatment":[21],"planning,":[22],"and":[23,50,110,136,179,190,195,198,218,230],"monitoring":[24],"therapeutic":[25],"outcomes":[26],"for":[27,141],"brain":[28],"cancer.":[29],"Despite":[30],"success":[32],"learning":[35,66],"algorithms":[36],"segmentation,":[40],"challenges":[41],"remain":[42],"capturing":[44,107],"long-range":[45],"dependencies,":[46],"extracting":[47],"relevant":[48],"features,":[49],"addressing":[51],"intensity":[52,155],"variations":[53],"across":[54,165,234],"different":[55],"imaging":[56],"modalities.":[57,166],"In":[58],"this":[59],"paper,":[60],"we":[61],"propose":[62],"a":[63,145],"novel":[64],"architecture,":[67],"Bias-Corrected":[68],"Twin":[69],"Squeeze-and-Excitation":[70],"Attention":[71],"Enhanced":[72],"UNet":[73,84,211],"(BC-TSEA-UNet),":[74],"which":[75],"integrates":[76],"twin":[77,95],"squeeze-and-excitation":[78],"(SE)":[79],"attention":[80],"blocks":[81,121],"into":[82],"backbone":[85],"to":[86,123,130,153,208,227],"tackle":[87],"these":[88],"challenges.":[89],"Unlike":[90],"standard":[91],"SE":[92,96,120],"blocks,":[93],"configuration":[97],"applies":[98],"dual":[99],"channel":[100],"recalibration":[101],"at":[102],"multiple":[103,235],"semantic":[104],"levels,":[105],"thereby":[106],"both":[108,134],"shallow":[109],"contextual":[112,138],"dependencies":[113],"more":[114,161,228],"effectively.":[115],"The":[116,167],"motivation":[117],"behind":[118],"incorporating":[119],"enhance":[124],"feature":[125,224],"recalibration,":[126],"allowing":[127],"model":[129],"better":[131],"focus":[132],"on":[133,173,204],"local":[135],"global":[137],"information":[139],"critical":[140],"segmentation.":[143],"Furthermore,":[144],"bias":[146],"correction":[147],"mechanism":[148],"employed":[150],"during":[151],"preprocessing":[152],"mitigate":[154],"non-uniformities":[156],"MRI":[158],"scans,":[159],"ensuring":[160],"consistent":[162],"data":[163],"representation":[164],"proposed":[168],"architecture":[169],"extensively":[171],"evaluated":[172],"BraTS":[175,177,180],"2019,":[176],"2020,":[178],"2023":[181],"datasets,":[182],"demonstrating":[183],"average":[184],"absolute":[185],"improvements":[186],"0.1112,":[188],"0.1339,":[189],"0.1986":[191],"Dice":[193],"scores":[194],"0.1601,":[196],"0.1396,":[197],"0.1847":[199],"mean":[201],"intersection-over-union":[202],"(IoU)":[203],"subregions":[206],"compared":[207],"baseline":[210],"model,":[212],"respectively.":[213],"By":[214],"emphasizing":[215],"salient":[216],"features":[217],"mitigating":[219],"bias,":[220],"BC-TSEA-UNet":[221],"significantly":[222],"improves":[223],"representation,":[225],"leading":[226],"reliable":[231],"datasets.":[236]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-06-15T08:34:33.830935","created_date":"2025-12-12T00:00:00"}
