{"id":"https://openalex.org/W4414957342","doi":"https://doi.org/10.1145/3649601.3698718","title":"CAU-Net: Chain Attention U-net for Breast Lesion Segmentation in Ultrasound Images","display_name":"CAU-Net: Chain Attention U-net for Breast Lesion Segmentation in Ultrasound Images","publication_year":2024,"publication_date":"2024-11-05","ids":{"openalex":"https://openalex.org/W4414957342","doi":"https://doi.org/10.1145/3649601.3698718"},"language":"en","primary_location":{"id":"doi:10.1145/3649601.3698718","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3649601.3698718","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3649601.3698718","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 International Conference on Research in Adaptive and Convergent Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3649601.3698718","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039550500","display_name":"Mingue Song","orcid":"https://orcid.org/0000-0002-0829-9380"},"institutions":[{"id":"https://openalex.org/I4322298","display_name":"Towson University","ror":"https://ror.org/044w7a341","country_code":"US","type":"education","lineage":["https://openalex.org/I4322298"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingue Song","raw_affiliation_strings":["Department of Computer &amp; Information Sciences, Towson University, Towson, MD, USA"],"raw_orcid":"https://orcid.org/0000-0002-0829-9380","affiliations":[{"raw_affiliation_string":"Department of Computer &amp; Information Sciences, Towson University, Towson, MD, USA","institution_ids":["https://openalex.org/I4322298"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060556922","display_name":"Yanggon Kim","orcid":"https://orcid.org/0000-0003-1860-6601"},"institutions":[{"id":"https://openalex.org/I4322298","display_name":"Towson University","ror":"https://ror.org/044w7a341","country_code":"US","type":"education","lineage":["https://openalex.org/I4322298"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanggon Kim","raw_affiliation_strings":["Towson University, Towson, USA"],"raw_orcid":"https://orcid.org/0000-0003-1860-6601","affiliations":[{"raw_affiliation_string":"Towson University, Towson, USA","institution_ids":["https://openalex.org/I4322298"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048517054","display_name":"Kwangmi Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I4322298","display_name":"Towson University","ror":"https://ror.org/044w7a341","country_code":"US","type":"education","lineage":["https://openalex.org/I4322298"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kwangmi Kim","raw_affiliation_strings":["Towson University, Towson, USA"],"raw_orcid":"https://orcid.org/0009-0006-9667-633X","affiliations":[{"raw_affiliation_string":"Towson University, Towson, USA","institution_ids":["https://openalex.org/I4322298"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4322298"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34517049,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"129","last_page":"134"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10862","display_name":"AI in cancer detection","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9965000152587891,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9912999868392944,"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":0.7386999726295471},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6883000135421753},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5565999746322632},{"id":"https://openalex.org/keywords/breast-ultrasound","display_name":"Breast ultrasound","score":0.5054000020027161},{"id":"https://openalex.org/keywords/lesion","display_name":"Lesion","score":0.45410001277923584},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.444599986076355},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4413999915122986},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.40389999747276306},{"id":"https://openalex.org/keywords/ultrasound","display_name":"Ultrasound","score":0.38119998574256897}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7386999726295471},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.725600004196167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7055000066757202},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6883000135421753},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5565999746322632},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5268999934196472},{"id":"https://openalex.org/C2777423100","wikidata":"https://www.wikidata.org/wiki/Q1888238","display_name":"Breast ultrasound","level":5,"score":0.5054000020027161},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.45410001277923584},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.444599986076355},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4413999915122986},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.40389999747276306},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.38119998574256897},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C2777432617","wikidata":"https://www.wikidata.org/wiki/Q22905905","display_name":"Breast imaging","level":5,"score":0.3330000042915344},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C2986892559","wikidata":"https://www.wikidata.org/wiki/Q234904","display_name":"Ultrasound imaging","level":3,"score":0.2888999879360199},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2854999899864197},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C79897977","wikidata":"https://www.wikidata.org/wiki/Q5054568","display_name":"Causal chain","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.251800000667572},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3649601.3698718","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3649601.3698718","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3649601.3698718","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 International Conference on Research in Adaptive and Convergent Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3649601.3698718","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3649601.3698718","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3649601.3698718","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 International Conference on Research in Adaptive and Convergent Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414957342.pdf","grobid_xml":"https://content.openalex.org/works/W4414957342.grobid-xml"},"referenced_works_count":11,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2593463961","https://openalex.org/W2905338897","https://openalex.org/W2963794428","https://openalex.org/W3025800305","https://openalex.org/W3037414627","https://openalex.org/W3109852921","https://openalex.org/W3204201789","https://openalex.org/W3212672269","https://openalex.org/W4310595042","https://openalex.org/W4386873870"],"related_works":[],"abstract_inverted_index":{"Various":[0],"methods":[1],"have":[2,22],"been":[3],"discussed":[4],"to":[5,33,100,117,122,135],"address":[6],"computer-aided":[7],"diagnosis":[8],"systems":[9],"for":[10,54],"breast":[11,39],"lesion":[12,56,136],"segmentation.":[13],"While":[14],"the":[15,65,70,80,87],"advents":[16],"of":[17,38,58,96,104],"U-net":[18,47,89],"and":[19,61,68,76],"its":[20,124],"variants":[21],"demonstrated":[23],"powerful":[24],"abilities,":[25],"their":[26],"fixed-size":[27],"receptive":[28,72],"fields":[29,73],"are":[30,107],"still":[31],"vulnerable":[32],"adequately":[34],"segment":[35],"flexible":[36],"structures":[37],"lesions.":[40],"This":[41],"paper":[42],"proposes":[43],"a":[44,110],"Chain":[45],"Attention":[46],"(CAU-Net)":[48],"that":[49,129],"effectively":[50],"improves":[51],"contextual":[52],"information":[53,103],"precise":[55],"segmentation":[57,120],"variable":[59],"location":[60],"sizes.":[62],"We":[63,114],"replace":[64],"traditional":[66],"convolutions":[67],"subdivide":[69],"domain-specific":[71],"into":[74],"global":[75],"local":[77],"path":[78],"under":[79],"chain":[81],"attention":[82],"module.":[83],"The":[84,126],"backbone":[85],"follows":[86],"conventional":[88],"architecture.":[90],"Thus,":[91],"each":[92],"feature":[93],"hierarchy":[94],"consists":[95],"two":[97],"parallel":[98],"pathways":[99],"reflect":[101],"comprehensive":[102],"representations.":[105],"Experiments":[106],"evaluated":[108],"on":[109],"public":[111],"ultrasound":[112],"dataset.":[113],"compare":[115],"it":[116],"eleven":[118],"state-of-the-art":[119],"networks":[121],"demonstrate":[123],"effectiveness.":[125],"results":[127],"show":[128],"our":[130],"network":[131],"enables":[132],"better":[133],"robustness":[134],"segmentation,":[137],"outperforming":[138],"various":[139],"counterparts.":[140]},"counts_by_year":[],"updated_date":"2026-08-25T07:29:55.448023","created_date":"2025-10-10T00:00:00"}
