{"id":"https://openalex.org/W4385819767","doi":"https://doi.org/10.1109/access.2023.3304993","title":"MSCA-UNet: Multi-Scale Convolutional Attention UNet for Automatic Cell Counting Using Density Regression","display_name":"MSCA-UNet: Multi-Scale Convolutional Attention UNet for Automatic Cell Counting Using Density Regression","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4385819767","doi":"https://doi.org/10.1109/access.2023.3304993"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3304993","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3304993","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10216972.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10216972.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101406867","display_name":"Like Qian","orcid":"https://orcid.org/0009-0003-8177-3290"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Like Qian","raw_affiliation_strings":["Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, China"],"raw_orcid":"https://orcid.org/0009-0003-8177-3290","affiliations":[{"raw_affiliation_string":"Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, China","institution_ids":["https://openalex.org/I109935558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374757","display_name":"Wei Qian","orcid":"https://orcid.org/0000-0002-9563-721X"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Qian","raw_affiliation_strings":["Department of Electrical and Computer Engineering, The University of Texas at El Paso, El Paso, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, The University of Texas at El Paso, El Paso, TX, USA","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001008928","display_name":"Dingcheng Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dingcheng Tian","raw_affiliation_strings":["Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, China"],"raw_orcid":"https://orcid.org/0000-0002-2004-644X","affiliations":[{"raw_affiliation_string":"Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, China","institution_ids":["https://openalex.org/I109935558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100577900","display_name":"Yaqi Zhu","orcid":"https://orcid.org/0009-0002-2546-4795"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaqi Zhu","raw_affiliation_strings":["Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, China","institution_ids":["https://openalex.org/I109935558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100390101","display_name":"Heng Zhao","orcid":"https://orcid.org/0000-0003-2993-4686"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heng Zhao","raw_affiliation_strings":["Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo, China","institution_ids":["https://openalex.org/I109935558"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021827481","display_name":"Yudong Yao","orcid":"https://orcid.org/0000-0003-3868-0593"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yudong Yao","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0003-3868-0593","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA","institution_ids":["https://openalex.org/I108468826"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101406867"],"corresponding_institution_ids":["https://openalex.org/I109935558"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.9265,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.76930297,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"11","issue":null,"first_page":"85990","last_page":"86001"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9991999864578247,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9991999864578247,"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/T10862","display_name":"AI in cancer detection","score":0.9984999895095825,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7574646472930908},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7077251672744751},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6048031449317932},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5252361297607422},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5008397102355957},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.49457237124443054},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4906415045261383},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.44775551557540894},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4378150701522827},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38106122612953186},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36715084314346313},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15357306599617004},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12522011995315552}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7574646472930908},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7077251672744751},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6048031449317932},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5252361297607422},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5008397102355957},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.49457237124443054},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4906415045261383},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44775551557540894},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4378150701522827},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38106122612953186},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36715084314346313},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15357306599617004},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12522011995315552},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3304993","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3304993","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10216972.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a854d5ef95f5433f9cb9d0f88444f3cf","is_oa":true,"landing_page_url":"https://doaj.org/article/a854d5ef95f5433f9cb9d0f88444f3cf","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 85990-86001 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3304993","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3304993","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10216972.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385819767.pdf","grobid_xml":"https://content.openalex.org/works/W4385819767.grobid-xml"},"referenced_works_count":66,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1542079534","https://openalex.org/W1792334496","https://openalex.org/W1826236479","https://openalex.org/W1901129140","https://openalex.org/W1909455558","https://openalex.org/W1963969681","https://openalex.org/W1972383555","https://openalex.org/W2018838463","https://openalex.org/W2029021004","https://openalex.org/W2033284482","https://openalex.org/W2071978209","https://openalex.org/W2074987302","https://openalex.org/W2080481652","https://openalex.org/W2095705004","https://openalex.org/W2108598243","https://openalex.org/W2145983039","https://openalex.org/W2149251234","https://openalex.org/W2159551006","https://openalex.org/W2207893099","https://openalex.org/W2293083603","https://openalex.org/W2331143823","https://openalex.org/W2347064614","https://openalex.org/W2428185128","https://openalex.org/W2519965060","https://openalex.org/W2520304734","https://openalex.org/W2549811460","https://openalex.org/W2598666589","https://openalex.org/W2774740362","https://openalex.org/W2784289537","https://openalex.org/W2791360098","https://openalex.org/W2791845681","https://openalex.org/W2798122215","https://openalex.org/W2892648298","https://openalex.org/W2899663614","https://openalex.org/W2900936384","https://openalex.org/W2963826106","https://openalex.org/W2964285767","https://openalex.org/W2972885242","https://openalex.org/W2981082221","https://openalex.org/W3036707525","https://openalex.org/W3103117538","https://openalex.org/W3103391201","https://openalex.org/W3108883671","https://openalex.org/W3163900027","https://openalex.org/W3170293501","https://openalex.org/W3177504760","https://openalex.org/W3204065694","https://openalex.org/W4206467037","https://openalex.org/W4284975071","https://openalex.org/W4285098026","https://openalex.org/W4296544717","https://openalex.org/W4312225519","https://openalex.org/W4385346076","https://openalex.org/W6631190155","https://openalex.org/W6632569617","https://openalex.org/W6638106647","https://openalex.org/W6639824700","https://openalex.org/W6674330103","https://openalex.org/W6681368121","https://openalex.org/W6748972969","https://openalex.org/W6750469568","https://openalex.org/W6755977528","https://openalex.org/W6811173682","https://openalex.org/W6842806116","https://openalex.org/W6847258573"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"The":[0],"quantification":[1],"of":[2,33,57,63,93,149,158],"cell":[3,27,44,53,58,150,211],"numbers":[4],"in":[5,12,52,89,184,196],"microscopy":[6,90],"images":[7],"plays":[8],"a":[9,77,102],"vital":[10],"role":[11],"biomedical":[13],"research":[14],"and":[15,30,60,79,152,170,182,192,225],"medical":[16],"diagnosis.":[17],"Presently,":[18],"deep":[19,81],"regression":[20,118],"networks":[21],"are":[22],"widely":[23],"employed":[24],"to":[25,49,69,165,188],"generate":[26],"density":[28,40,111,117],"maps,":[29],"the":[31,39,50,55,61,71,147,156,159,163,173,176,185,214,219,226],"number":[32],"cells":[34,88],"is":[35],"obtained":[36],"by":[37],"integrating":[38],"maps.":[41],"However,":[42],"automating":[43],"counting":[45],"remains":[46],"challenging":[47],"due":[48],"variability":[51],"morphology,":[54],"diversity":[56],"types,":[59],"interference":[62],"image":[64,190],"backgrounds.":[65],"This":[66],"paper":[67],"aims":[68],"address":[70],"central":[72],"question:":[73],"\u2019Can":[74],"we":[75,100,127],"design":[76,157],"robust":[78],"efficient":[80],"learning":[82],"model":[83],"that":[84],"can":[85],"effectively":[86],"count":[87],"images,":[91],"regardless":[92],"these":[94],"challenges?\u2019":[95],"To":[96],"tackle":[97],"this":[98],"issue,":[99],"propose":[101],"novel":[103],"multi-scale":[104,133],"convolutional":[105,180],"attention":[106],"UNet":[107,161],"(MSCA-UNet)":[108],"based":[109],"on":[110,206],"regression.":[112],"Compared":[113],"with":[114,132,143],"other":[115,203],"advanced":[116,204],"methods,":[119],"our":[120],"method":[121,201],"introduces":[122],"two":[123],"key":[124],"innovations.":[125],"Firstly,":[126],"employ":[128],"an":[129,137],"MSCA":[130],"block":[131],"interaction":[134],"ability":[135],"as":[136],"encoder":[138,164],"component,":[139],"which,":[140],"when":[141],"combined":[142],"spatial":[144,153],"attention,":[145],"enhances":[146],"extraction":[148],"details":[151,191],"information.":[154],"Secondly,":[155],"asymmetric":[160],"allows":[162],"extract":[166],"more":[167],"global":[168],"information":[169],"better":[171],"understand":[172],"image.":[174],"In":[175],"meantime,":[177],"using":[178],"smaller":[179],"kernels":[181],"strides":[183],"decoder":[186],"helps":[187],"restore":[189],"edge":[193],"information,":[194],"resulting":[195],"improved":[197],"network":[198],"performance.":[199],"Our":[200],"outperformed":[202],"methods":[205],"three":[207],"publicly":[208],"available":[209],"benchmark":[210],"datasets,":[212],"including":[213],"synthetic":[215],"bacterial":[216],"(VGG)":[217],"dataset,":[218,224],"modified":[220],"bone":[221],"marrow":[222],"(MBM)":[223],"human":[227],"subcutaneous":[228],"adipose":[229],"tissue":[230],"(ADI)":[231],"dataset.":[232]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}