{"id":"https://openalex.org/W4408609524","doi":"https://doi.org/10.1109/access.2025.3552847","title":"Human Brain-Inspired Network Using Transformer and Feedback Processing for Cell Image Segmentation","display_name":"Human Brain-Inspired Network Using Transformer and Feedback Processing for Cell Image Segmentation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4408609524","doi":"https://doi.org/10.1109/access.2025.3552847"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3552847","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3552847","pdf_url":null,"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://doi.org/10.1109/access.2025.3552847","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5107404759","display_name":"Hinako Mitsuoka","orcid":null},"institutions":[{"id":"https://openalex.org/I96636082","display_name":"Meijo University","ror":"https://ror.org/04h42fc75","country_code":"JP","type":"education","lineage":["https://openalex.org/I96636082"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Hinako Mitsuoka","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Meijo University, Nagoya, Japan","Meijo University, Nagoya, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Meijo University, Nagoya, Japan","institution_ids":["https://openalex.org/I96636082"]},{"raw_affiliation_string":"Meijo University, Nagoya, Japan","institution_ids":["https://openalex.org/I96636082"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103163418","display_name":"Kazuhiro Hotta","orcid":"https://orcid.org/0000-0002-5675-8713"},"institutions":[{"id":"https://openalex.org/I96636082","display_name":"Meijo University","ror":"https://ror.org/04h42fc75","country_code":"JP","type":"education","lineage":["https://openalex.org/I96636082"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuhiro Hotta","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Meijo University, Nagoya, Japan","Meijo University, Nagoya, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Meijo University, Nagoya, Japan","institution_ids":["https://openalex.org/I96636082"]},{"raw_affiliation_string":"Meijo University, Nagoya, Japan","institution_ids":["https://openalex.org/I96636082"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5107404759"],"corresponding_institution_ids":["https://openalex.org/I96636082"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07976259,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"50918","last_page":"50930"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9648000001907349,"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":0.9648000001907349,"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/T10320","display_name":"Neural Networks and Applications","score":0.9070000052452087,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7215767502784729},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6076008677482605},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5785016417503357},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5636913180351257},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5470484495162964},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5014119148254395},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.42051011323928833},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35860419273376465},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2836403250694275},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.09088090062141418},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07390108704566956},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.07281652092933655}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7215767502784729},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6076008677482605},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5785016417503357},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5636913180351257},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5470484495162964},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5014119148254395},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.42051011323928833},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35860419273376465},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2836403250694275},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.09088090062141418},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07390108704566956},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.07281652092933655}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3552847","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3552847","pdf_url":null,"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:a94ab2595026453f830cd98f50ae5a8a","is_oa":true,"landing_page_url":"https://doaj.org/article/a94ab2595026453f830cd98f50ae5a8a","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 50918-50930 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3552847","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3552847","pdf_url":null,"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":[{"id":"https://openalex.org/G3372071235","display_name":null,"funder_award_id":"24K15020","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1934184906","https://openalex.org/W2044511081","https://openalex.org/W2098580305","https://openalex.org/W2120907531","https://openalex.org/W2194775991","https://openalex.org/W2412782625","https://openalex.org/W2560023338","https://openalex.org/W2560311620","https://openalex.org/W2787091153","https://openalex.org/W2910628332","https://openalex.org/W2948510860","https://openalex.org/W2959397617","https://openalex.org/W2962914239","https://openalex.org/W2963574983","https://openalex.org/W2963881378","https://openalex.org/W3014641072","https://openalex.org/W3127751679","https://openalex.org/W3128395113","https://openalex.org/W3131500599","https://openalex.org/W3133281654","https://openalex.org/W3138516171","https://openalex.org/W3170841864","https://openalex.org/W3178709797","https://openalex.org/W3181095825","https://openalex.org/W3196890347","https://openalex.org/W4312815172","https://openalex.org/W4312820606","https://openalex.org/W4312960790","https://openalex.org/W4381795072","https://openalex.org/W4385245566","https://openalex.org/W4386076267","https://openalex.org/W4388145401","https://openalex.org/W4390873750","https://openalex.org/W6726497184","https://openalex.org/W6739696289","https://openalex.org/W6748775820","https://openalex.org/W6756444276","https://openalex.org/W6795297120","https://openalex.org/W6796931752","https://openalex.org/W6797399245","https://openalex.org/W6855196175","https://openalex.org/W6861962099","https://openalex.org/W6864221053","https://openalex.org/W6872423475"],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W4389858081","https://openalex.org/W2501551404","https://openalex.org/W4298131179","https://openalex.org/W2113201962","https://openalex.org/W4385583601","https://openalex.org/W4395685956","https://openalex.org/W3159516372","https://openalex.org/W4398146871","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,197,294,331],"of":[2,106,129,137,157,181,215,232,247],"microscopy":[3],"cell":[4,17,39,305,324],"images":[5],"by":[6,126,145,154,163,209,258],"deep":[7],"learning":[8],"plays":[9],"a":[10,119,196,275,356],"crucial":[11],"role":[12],"in":[13,29,57,62,102,159,185,250,323],"advancing":[14],"medicine":[15],"and":[16,36,60,71,86,175,255,267],"biology":[18],"research.":[19],"We":[20,269],"considered":[21],"that":[22,99,199,286,309],"Transformers,":[23],"which":[24],"have":[25,73],"recently":[26],"outperformed":[27],"CNNs":[28,72],"image":[30,40,306,325],"recognition,":[31],"could":[32,110],"also":[33],"be":[34,111],"improved":[35],"developed":[37],"for":[38,122],"segmentation.":[41,326],"Transformers":[42,70,201],"tend":[43],"to":[44,76,90,150,218,240,262,279],"focus":[45],"more":[46,346],"on":[47,51,302],"contextual":[48],"information":[49,158,170,228],"than":[50,338,348],"detailed":[52,227],"information.":[53],"This":[54,179],"often":[55],"results":[56],"blurred":[58],"boundaries":[59,266],"difficulty":[61],"distinguishing":[63],"small":[64,256],"cellular":[65],"structures.":[66],"Hybrid":[67],"models":[68,285],"combining":[69],"been":[74],"proposed":[75,115,311],"address":[77],"this":[78,167,291],"issue,":[79],"but":[80],"they":[81],"introduce":[82],"high":[83],"computational":[84,299,336],"costs":[85],"architectural":[87],"complexity.":[88],"Therefore,":[89],"supplement":[91],"or":[92,173,354],"reinforce":[93],"the":[94,103,107,127,130,134,138,141,155,160,212,216,219,230,233,241,245,260,310,314,351],"missing":[95],"information,":[96],"we":[97,191],"hypothesized":[98],"feedback":[100,164,193,281,317,340],"processing":[101,147,194,206],"visual":[104,135],"cortex":[105,136],"human":[108,131,139],"brain":[109],"highly":[112],"effective.":[113],"Our":[114,327,342],"Feedback":[116,205,273],"Former":[117],"is":[118,143,171,177,207,238],"novel":[120],"architecture":[121],"semantic":[123],"segmentation,":[124,248],"inspired":[125],"structure":[128,358],"brain.":[132],"In":[133,166],"brain,":[140],"inference":[142,237],"made":[144],"feedforward":[146],"from":[148],"lower":[149,220,242,298],"upper":[151],"layers,":[152],"followed":[153],"transfer":[156],"reverse":[161],"direction":[162],"processing.":[165],"process,":[168],"specific":[169],"emphasized":[172],"suppressed,":[174],"recognition":[176,182],"modified.":[178],"modification":[180],"might":[183],"occur":[184],"neural":[186],"networks":[187],"as":[188,202],"well,":[189],"so":[190],"incorporate":[192],"into":[195],"model":[198,217,234,352],"uses":[200],"an":[203],"encoder.":[204],"implemented":[208],"directly":[210],"connecting":[211],"output":[213,231],"neighborhood":[214],"layers.":[221],"Feeding":[222],"back":[223],"feature":[224],"maps":[225],"with":[226,252,359],"near":[229,265],"obtained":[235],"once":[236],"performed":[239],"layers":[243],"improves":[244,293],"accuracy":[246,295,322,332,345],"especially":[249],"areas":[251],"complex":[253],"textures":[254],"objects,":[257],"enhancing":[259],"ability":[261],"extract":[263],"features":[264],"details.":[268],"further":[270],"propose":[271],"Lite":[272],"Module,":[274],"computationally":[276],"efficient":[277],"alternative":[278],"conventional":[280,339],"modules.":[282],"Unlike":[283],"hybrid":[284,357],"require":[287],"additional":[288],"CNN":[289],"components,":[290],"module":[292],"while":[296,333],"maintaining":[297],"costs.":[300],"Experiments":[301],"three":[303],"different":[304],"datasets":[307],"confirmed":[308],"method":[312,328,343],"surpasses":[313],"methods":[315],"without":[316],"processing,":[318],"demonstrating":[319],"its":[320],"superior":[321],"achieved":[329],"higher":[330],"consuming":[334],"less":[335],"cost":[337],"approaches.":[341],"enhanced":[344],"efficiently":[347],"simply":[349],"increasing":[350],"size":[353],"using":[355],"CNNs.":[360]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-03-20T00:00:00"}
