{"id":"https://openalex.org/W4313427442","doi":"https://doi.org/10.1109/bibm55620.2022.9995702","title":"A Hybrid Cross-Scale Transformer Architecture for Robust Medical Image Segmentation","display_name":"A Hybrid Cross-Scale Transformer Architecture for Robust Medical Image Segmentation","publication_year":2022,"publication_date":"2022-12-06","ids":{"openalex":"https://openalex.org/W4313427442","doi":"https://doi.org/10.1109/bibm55620.2022.9995702"},"language":"en","primary_location":{"id":"doi:10.1109/bibm55620.2022.9995702","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm55620.2022.9995702","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100686910","display_name":"Ning Yang","orcid":"https://orcid.org/0009-0002-3684-9009"},"institutions":[{"id":"https://openalex.org/I44445938","display_name":"Shandong Jianzhu University","ror":"https://ror.org/01gbfax37","country_code":"CN","type":"education","lineage":["https://openalex.org/I44445938"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Ning","raw_affiliation_strings":["Shandong Jianzhu University,School of Computer Science and Technology,Jinan,China","School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Jianzhu University,School of Computer Science and Technology,Jinan,China","institution_ids":["https://openalex.org/I44445938"]},{"raw_affiliation_string":"School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China","institution_ids":["https://openalex.org/I44445938"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101952623","display_name":"Shouyi Zhang","orcid":"https://orcid.org/0000-0003-1951-2244"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shouyi Zhang","raw_affiliation_strings":["The University of Edinburgh,School of Informatics,Edinburgh,UK","School of Informatics, The University of Edinburgh, Edinburgh, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Edinburgh,School of Informatics,Edinburgh,UK","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"School of Informatics, The University of Edinburgh, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031172391","display_name":"Zhong Wei","orcid":"https://orcid.org/0000-0002-7967-4897"},"institutions":[{"id":"https://openalex.org/I4210147983","display_name":"Guangdong Police College","ror":"https://ror.org/05krxyw16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210147983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhong","raw_affiliation_strings":["Guangdong Police College,Department of Network Information Security,Guangzhou,China","Department of Network Information Security, Guangdong Police College, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangdong Police College,Department of Network Information Security,Guangzhou,China","institution_ids":["https://openalex.org/I4210147983"]},{"raw_affiliation_string":"Department of Network Information Security, Guangdong Police College, Guangzhou, China","institution_ids":["https://openalex.org/I4210147983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089438496","display_name":"\u041f\u044d\u0439\u0434\u044d \u041b\u044e","orcid":"https://orcid.org/0000-0001-5048-8145"},"institutions":[{"id":"https://openalex.org/I59483232","display_name":"Shandong University of Finance and Economics","ror":"https://ror.org/02e2nnq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I59483232"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peide Liu","raw_affiliation_strings":["Shandong University of Finance and Economics,School of Management Science and Engineering,Jinan,China","School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong University of Finance and Economics,School of Management Science and Engineering,Jinan,China","institution_ids":["https://openalex.org/I59483232"]},{"raw_affiliation_string":"School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, China","institution_ids":["https://openalex.org/I59483232"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101753050","display_name":"Caiming Zhang","orcid":"https://orcid.org/0000-0003-0217-1543"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Caiming Zhang","raw_affiliation_strings":["Shandong University,School of Software,Jinan,China","School of Software, Shandong University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong University,School of Software,Jinan,China","institution_ids":["https://openalex.org/I154099455"]},{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.19358087,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1677","last_page":"1682"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9990000128746033,"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":0.9987000226974487,"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/computer-science","display_name":"Computer science","score":0.742946207523346},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6714568734169006},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5353090763092041},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5306999087333679},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5068631768226624},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.5057194232940674},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5000934600830078},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.481975257396698},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45964834094047546},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35251691937446594},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11560171842575073}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.742946207523346},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6714568734169006},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5353090763092041},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5306999087333679},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5068631768226624},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5057194232940674},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5000934600830078},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.481975257396698},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45964834094047546},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35251691937446594},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11560171842575073},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm55620.2022.9995702","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm55620.2022.9995702","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6600000262260437}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null},{"id":"https://openalex.org/F4320324777","display_name":"Shandong Jianzhu University","ror":"https://ror.org/01gbfax37"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2108598243","https://openalex.org/W2560023338","https://openalex.org/W2884436604","https://openalex.org/W2885143946","https://openalex.org/W2888358068","https://openalex.org/W2914957207","https://openalex.org/W2964227007","https://openalex.org/W2981413347","https://openalex.org/W2986445973","https://openalex.org/W3119734611","https://openalex.org/W4205735002","https://openalex.org/W6639824700"],"related_works":["https://openalex.org/W2770593030","https://openalex.org/W3154990682","https://openalex.org/W2560201613","https://openalex.org/W2171975302","https://openalex.org/W2022352247","https://openalex.org/W4312219546","https://openalex.org/W2377538627","https://openalex.org/W2107220315","https://openalex.org/W1996690921","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Transformer":[0,31],"architecture":[1,33,157],"has":[2,118],"emerged":[3],"to":[4,16,81,105,121,160,192],"be":[5,161],"successful":[6],"in":[7,75],"many":[8],"natural":[9],"language":[10],"processing":[11],"tasks.":[12],"However,":[13],"its":[14],"applications":[15],"clinical":[17],"practice":[18],"remain":[19],"largely":[20],"unexplored.":[21],"In":[22],"this":[23],"study,":[24],"we":[25,54,100],"propose":[26,55],"a":[27,186],"Robust":[28],"Cross-Scale":[29],"Hybrid":[30],"(RCSHT)":[32],"for":[34],"medical":[35,197],"image":[36,198],"segmentation,":[37],"which":[38,67],"can":[39],"effectively":[40,82,94],"enhance":[41,83,128],"the":[42,61,84,96,110,116,129,138,141,147,182,190],"multi-scale":[43,91],"feature":[44,148],"representations":[45,149],"while":[46],"integrating":[47],"local":[48],"features":[49],"with":[50],"global":[51],"dependencies.":[52],"Specifically,":[53],"two":[56,111,139],"new":[57],"modules":[58],"based":[59],"on":[60,72,109,171,195],"self-attention":[62],"mechanism:":[63],"PSCM":[64],"and":[65,77,93,126,143],"PCCM,":[66],"perform":[68,106],"matrix":[69],"low-rank":[70],"transformation":[71],"cross-scale":[73],"images":[74,92],"spatial":[76,102,107,124,130],"channel":[78],"space,":[79],"respectively,":[80],"discriminant":[85,144],"ability":[86,145],"of":[87,90,137,146],"long-range":[88],"dependencies":[89],"reduce":[95],"computational":[97],"complexity.":[98],"Meanwhile,":[99],"use":[101],"positional":[103],"encoding":[104],"regularization":[108],"attention":[112],"modules,":[113,140],"so":[114],"that":[115,154],"model":[117],"more":[119],"opportunities":[120],"obtain":[122],"valuable":[123],"details":[125],"further":[127,151],"retention":[131],"ability.":[132],"Besides,":[133],"by":[134,185],"fusing":[135],"outputs":[136],"robustness":[142],"are":[150],"improved.":[152],"Note":[153],"our":[155,172,178],"hybrid":[156],"allows":[158],"transformers":[159],"initialized":[162],"as":[163],"convolutional":[164],"networks":[165],"without":[166],"pre-training.":[167],"Extensive":[168],"experiments":[169],"conducted":[170],"newly":[173],"proposed":[174],"dataset":[175],"have":[176],"demonstrated":[177],"RCSHT":[179],"notably":[180],"outperforms":[181],"state-of-the-art":[183],"methods":[184],"large":[187],"margin,":[188],"holding":[189],"promise":[191],"generalize":[193],"well":[194],"other":[196],"segmentation.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}