{"id":"https://openalex.org/W4408355278","doi":"https://doi.org/10.1109/icassp49660.2025.10889774","title":"CAAL-Unet: a Confusion Area Attention Lightweight Network for Brain Tumor Segmentation","display_name":"CAAL-Unet: a Confusion Area Attention Lightweight Network for Brain Tumor Segmentation","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408355278","doi":"https://doi.org/10.1109/icassp49660.2025.10889774"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10889774","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889774","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5075535970","display_name":"Pu Yang","orcid":"https://orcid.org/0000-0003-2283-8835"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yang Pu","raw_affiliation_strings":["Xiamen University,School of Film,Xiamen,China"],"affiliations":[{"raw_affiliation_string":"Xiamen University,School of Film,Xiamen,China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101590561","display_name":"Qingfeng Wu","orcid":"https://orcid.org/0009-0008-8784-9256"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingfeng Wu","raw_affiliation_strings":["Xiamen University,Xiamen,China"],"affiliations":[{"raw_affiliation_string":"Xiamen University,Xiamen,China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5075535970"],"corresponding_institution_ids":["https://openalex.org/I191208505"],"apc_list":null,"apc_paid":null,"fwci":0.898,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.71899552,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9990000128746033,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9817000031471252,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9603999853134155,"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/confusion","display_name":"Confusion","score":0.8373558521270752},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6400331258773804},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5552941560745239},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32460537552833557},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.16809168457984924}],"concepts":[{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.8373558521270752},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6400331258773804},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5552941560745239},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32460537552833557},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.16809168457984924},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10889774","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889774","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1884191083","https://openalex.org/W1901129140","https://openalex.org/W2464708700","https://openalex.org/W2531409750","https://openalex.org/W2921406441","https://openalex.org/W2962914239","https://openalex.org/W2963125010","https://openalex.org/W3011743383","https://openalex.org/W3014974815","https://openalex.org/W3035414587","https://openalex.org/W3092344722","https://openalex.org/W3094502228","https://openalex.org/W3135385363","https://openalex.org/W3204614423","https://openalex.org/W3212933375","https://openalex.org/W4212875960","https://openalex.org/W4221163766","https://openalex.org/W4289752563","https://openalex.org/W4302275239","https://openalex.org/W4386047745","https://openalex.org/W4391274659","https://openalex.org/W6638667902","https://openalex.org/W6750469568","https://openalex.org/W6790275670","https://openalex.org/W6845563047"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2964976023","https://openalex.org/W1982477181","https://openalex.org/W2403083015","https://openalex.org/W4285488523","https://openalex.org/W2011367623","https://openalex.org/W2734065904","https://openalex.org/W2027125286"],"abstract_inverted_index":{"Brain":[0,169],"tumors":[1,14],"are":[2,164],"one":[3],"of":[4,8,12,24,30,126,136,142,146,155,161],"the":[5,19,63,91,103,116,123,127,140,143,147,162,167],"deadliest":[6],"types":[7],"cancer.":[9,25],"Accurate":[10],"segmentation":[11,195],"brain":[13,137],"is":[15,48,112,149],"very":[16],"important":[17],"for":[18],"diagnosis,":[20],"evaluation":[21],"and":[22,38,66,98,159,190],"treatment":[23],"Recently,":[26],"a":[27,77,106],"large":[28],"number":[29],"deep":[31,153],"learning-based":[32],"methods":[33],"have":[34],"replaced":[35],"manual":[36],"annotation":[37],"achieved":[39],"remarkable":[40],"results.":[41,196],"However,":[42],"most":[43],"current":[44],"research":[45],"on":[46,81,166],"CNNs":[47],"devoted":[49],"to":[50,56,89,114,118,122,129,151],"creating":[51],"increasingly":[52],"complex":[53],"convolutional":[54],"modules":[55],"improve":[57],"performance,":[58],"which":[59,69],"in":[60],"turn":[61],"increases":[62],"computational":[64],"cost":[65],"model":[67,117,163],"complexity,":[68],"hinders":[70],"its":[71],"clinical":[72],"application.":[73],"This":[74],"work":[75],"proposes":[76],"effective":[78],"CNN":[79],"based":[80],"3DUnet,":[82],"CAAL-Unet.":[83],"It":[84],"uses":[85],"3D":[86],"partical":[87],"Convolution":[88],"replace":[90],"traditional":[92],"convolution,":[93],"thereby":[94],"reducing":[95],"network":[96,148],"parameters":[97,189],"redundant":[99],"feature":[100,133],"maps.":[101],"In":[102],"upsampling":[104],"stage,":[105],"new":[107],"confusion":[108],"area":[109],"attention":[110,121],"module":[111],"added":[113],"enable":[115],"pay":[119],"more":[120],"edge":[124],"part":[125],"segmentation,":[128],"achieve":[130,152],"multi-scale":[131],"semantic":[132],"information":[134],"extraction":[135],"tumors.":[138],"And":[139],"output":[141],"higher":[144],"level":[145],"supervised":[150],"injection":[154],"gradients.":[156],"The":[157,175],"performance":[158],"complexity":[160],"evaluated":[165],"Multimodal":[168],"Tumor":[170],"Segmentation":[171],"Challenge":[172],"(BraTS2021)":[173],"dataset.":[174],"results":[176],"show":[177],"that":[178],"compared":[179],"with":[180],"other":[181],"high-performance":[182],"methods,":[183],"our":[184],"method":[185],"has":[186],"significantly":[187],"reduced":[188],"FLOPs,":[191],"while":[192],"maintaining":[193],"competitive":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}
