{"id":"https://openalex.org/W4410297103","doi":"https://doi.org/10.1109/isbi60581.2025.10981108","title":"EViT-UNET: U-Net Like Efficient Vision Transformer for Medical Image Segmentation on Mobile and Edge Devices","display_name":"EViT-UNET: U-Net Like Efficient Vision Transformer for Medical Image Segmentation on Mobile and Edge Devices","publication_year":2025,"publication_date":"2025-04-14","ids":{"openalex":"https://openalex.org/W4410297103","doi":"https://doi.org/10.1109/isbi60581.2025.10981108","pmid":"https://pubmed.ncbi.nlm.nih.gov/40791942"},"language":"en","primary_location":{"id":"doi:10.1109/isbi60581.2025.10981108","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi60581.2025.10981108","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12337706","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100353715","display_name":"Xin Li","orcid":"https://orcid.org/0000-0001-6449-4044"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xin Li","raw_affiliation_strings":["School of Computing and Augmented Intelligence, Arizona State University,AZ,USA,85281"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Augmented Intelligence, Arizona State University,AZ,USA,85281","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100439574","display_name":"Wenhui Zhu","orcid":"https://orcid.org/0009-0000-5207-6283"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenhui Zhu","raw_affiliation_strings":["School of Computing and Augmented Intelligence, Arizona State University,AZ,USA,85281"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Augmented Intelligence, Arizona State University,AZ,USA,85281","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xuanzhao Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuanzhao Dong","raw_affiliation_strings":["School of Computing and Augmented Intelligence, Arizona State University,AZ,USA,85281"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Augmented Intelligence, Arizona State University,AZ,USA,85281","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103078397","display_name":"Oana M. Dumitrascu","orcid":"https://orcid.org/0000-0003-2033-449X"},"institutions":[{"id":"https://openalex.org/I4210125099","display_name":"Mayo Clinic in Arizona","ror":"https://ror.org/03jp40720","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210125099"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Oana M. Dumitrascu","raw_affiliation_strings":["Mayo Clinic,Department of Neurology,Scottsdale,AZ,USA,85251"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mayo Clinic,Department of Neurology,Scottsdale,AZ,USA,85251","institution_ids":["https://openalex.org/I4210125099"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100740828","display_name":"Yalin Wang","orcid":"https://orcid.org/0000-0002-6241-735X"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yalin Wang","raw_affiliation_strings":["School of Computing and Augmented Intelligence, Arizona State University,AZ,USA,85281"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Augmented Intelligence, Arizona State University,AZ,USA,85281","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100353715"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":1.3189,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.80368048,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"2025","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":0.9038000106811523,"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":0.9038000106811523,"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-vision","display_name":"Computer vision","score":0.7154585123062134},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6974503397941589},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6886314153671265},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6181118488311768},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4886375069618225},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.45341193675994873},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4401324987411499},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1287195384502411},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.11360371112823486},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.07250121235847473}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7154585123062134},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6974503397941589},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6886314153671265},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6181118488311768},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4886375069618225},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.45341193675994873},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4401324987411499},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1287195384502411},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.11360371112823486},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.07250121235847473}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/isbi60581.2025.10981108","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi60581.2025.10981108","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},{"id":"pmid:40791942","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40791942","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. IEEE International Symposium on Biomedical Imaging","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:12337706","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12337706","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc IEEE Int Symp Biomed Imaging","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:12337706","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12337706","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc IEEE Int Symp Biomed Imaging","raw_type":"Text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5673663715","display_name":null,"funder_award_id":"R01EY032125","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1986655823","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W3011384228","https://openalex.org/W2124969951","https://openalex.org/W2945274617","https://openalex.org/W4313052709","https://openalex.org/W4298131179","https://openalex.org/W2375430703"],"abstract_inverted_index":{"With":[0],"the":[1,53,63],"rapid":[2],"development":[3],"of":[4,39,49,55,67],"deep":[5],"learning,":[6],"CNN-based":[7],"U-shaped":[8,120],"networks":[9],"have":[10],"succeeded":[11],"in":[12,26,33,58,148],"medical":[13,59,74,113,149],"image":[14,60,150],"segmentation":[15,35,99,151],"and":[16,51,76,87,128],"are":[17],"widely":[18],"applied":[19],"for":[20,46,72,111],"various":[21],"tasks.":[22,36],"However,":[23,62],"their":[24,31],"limitations":[25],"capturing":[27],"global":[28],"features":[29],"hinder":[30],"performance":[32],"complex":[34],"The":[37,157],"rise":[38],"Vision":[40],"Transformer":[41],"(ViT)":[42],"has":[43],"effectively":[44],"compensated":[45],"this":[47],"deficiency":[48],"CNNs":[50],"promoted":[52],"application":[54],"ViT-based":[56,98],"U-networks":[57],"segmentation.":[61],"high":[64,146],"computational":[65,103,155],"demands":[66],"ViT":[68],"make":[69],"it":[70,109],"unsuitable":[71],"many":[73],"devices":[75],"mobile":[77],"platforms":[78],"with":[79,134],"limited":[80],"resources,":[81],"restricting":[82],"its":[83],"deployment":[84],"on":[85,118],"resource-constrained":[86,112],"edge":[88],"devices.":[89,114],"To":[90],"address":[91],"this,":[92],"we":[93],"propose":[94],"EViT-UNet,":[95],"an":[96,123],"efficient":[97],"network":[100],"that":[101,143],"reduces":[102],"complexity":[104],"while":[105,152],"maintaining":[106],"accuracy,":[107],"making":[108],"ideal":[110],"EViT-UNet":[115,144],"is":[116,159],"built":[117],"a":[119],"architecture,":[121],"comprising":[122],"encoder,":[124],"decoder,":[125],"bottleneck":[126],"layer,":[127],"skip":[129],"connections,":[130],"combining":[131],"convolutional":[132],"operations":[133],"self-attention":[135],"mechanisms":[136],"to":[137],"optimize":[138],"efficiency.":[139],"Experimental":[140],"results":[141],"demonstrate":[142],"achieves":[145],"accuracy":[147],"significantly":[153],"reducing":[154],"complexity.":[156],"code":[158],"available":[160],"at":[161],"https://github.com/Retinal-Research/EVIT-UNET.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}
