{"id":"https://openalex.org/W4400491197","doi":"https://doi.org/10.1109/cscwd61410.2024.10580197","title":"Towards Efficient Sparse Transformer based Medical Image Registration","display_name":"Towards Efficient Sparse Transformer based Medical Image Registration","publication_year":2024,"publication_date":"2024-05-08","ids":{"openalex":"https://openalex.org/W4400491197","doi":"https://doi.org/10.1109/cscwd61410.2024.10580197"},"language":"en","primary_location":{"id":"doi:10.1109/cscwd61410.2024.10580197","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cscwd61410.2024.10580197","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-article"},"type":"article","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/A5017279444","display_name":"Haifeng Zhao","orcid":"https://orcid.org/0000-0001-5761-4941"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haifeng Zhao","raw_affiliation_strings":["Anhui University,School of Computer Science and Technology,Hefei,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Anhui University,School of Computer Science and Technology,Hefei,China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101395912","display_name":"Quanshuang He","orcid":null},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quanshuang He","raw_affiliation_strings":["Anhui University,School of Computer Science and Technology,Hefei,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Anhui University,School of Computer Science and Technology,Hefei,China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086497042","display_name":"Deyin Liu","orcid":"https://orcid.org/0000-0002-0371-9921"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Deyin Liu","raw_affiliation_strings":["Anhui University,Anhui Provincial Key Laboratory of Multimodal Cognitive Computing,Hefei,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Anhui University,Anhui Provincial Key Laboratory of Multimodal Cognitive Computing,Hefei,China","institution_ids":["https://openalex.org/I143868143"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08871675,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":null,"first_page":"3098","last_page":"3103"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9950000047683716,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9950000047683716,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9944000244140625,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9782000184059143,"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/image-registration","display_name":"Image registration","score":0.7016524076461792},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6360208988189697},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5533252358436584},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5505530834197998},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5326091051101685},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3089878559112549},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1203879714012146},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.09966221451759338},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.08727839589118958}],"concepts":[{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.7016524076461792},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6360208988189697},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5533252358436584},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5505530834197998},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5326091051101685},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3089878559112549},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1203879714012146},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.09966221451759338},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.08727839589118958}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cscwd61410.2024.10580197","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cscwd61410.2024.10580197","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334897","display_name":"Natural Science Foundation of Anhui Province","ror":null},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1970928383","https://openalex.org/W2083099567","https://openalex.org/W2150534249","https://openalex.org/W2565639579","https://openalex.org/W2799597343","https://openalex.org/W2803224943","https://openalex.org/W2891631795","https://openalex.org/W2949846184","https://openalex.org/W2963299740","https://openalex.org/W2983976417","https://openalex.org/W3035201239","https://openalex.org/W3094502228","https://openalex.org/W3100018800","https://openalex.org/W3131500599","https://openalex.org/W3138516171","https://openalex.org/W3156621598","https://openalex.org/W3159663321","https://openalex.org/W3170642968","https://openalex.org/W4214493665","https://openalex.org/W4284970811","https://openalex.org/W4287022992","https://openalex.org/W4295312788","https://openalex.org/W4385245566","https://openalex.org/W4386071792","https://openalex.org/W4387185351","https://openalex.org/W6631190155","https://openalex.org/W6729983426","https://openalex.org/W6766978945","https://openalex.org/W6784333009","https://openalex.org/W6794262402","https://openalex.org/W6795308139","https://openalex.org/W6800217721","https://openalex.org/W6811230874"],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Deformable":[0],"medical":[1,33,67,83,223],"image":[2,34,68,84,224],"registration":[3,69,225],"is":[4],"a":[5,131,152,171],"crucial":[6],"task":[7],"that":[8,94,156,184,214],"involves":[9],"extracting":[10],"and":[11,37,144,159,209],"aligning":[12],"features":[13],"from":[14],"two":[15],"images":[16],"to":[17,48,135,179,188],"establish":[18],"precise":[19],"correspondence,":[20],"essentially":[21],"for":[22,82,166],"accurate":[23],"registration.":[24,85],"While":[25],"visual":[26],"transformers":[27],"have":[28],"propelled":[29],"recent":[30],"advancements":[31],"in":[32,65,98,121,198,221],"analysis,":[35],"training":[36],"inference":[38],"with":[39,105],"Transformers":[40,99],"can":[41],"become":[42],"excessively":[43],"computationally":[44],"expensive,":[45],"particularly":[46],"due":[47],"the":[49,92,111,122,161,177,186,210],"quadratic":[50],"complexity":[51],"of":[52,58,125],"self-attention":[53,96,164],"when":[54],"handling":[55],"long":[56],"sequences":[57],"representations.":[59],"This":[60,128,192],"challenge":[61],"becomes":[62],"more":[63],"pronounced":[64],"3D":[66],"tasks.":[70,226],"To":[71],"tackle":[72],"this":[73],"issue,":[74],"we":[75,109],"propose":[76,151],"an":[77],"efficient":[78],"Hierarchical":[79],"Pyramid":[80],"Converter":[81],"The":[86],"proposed":[87,216],"approach":[88],"firstly":[89],"capitalizes":[90],"on":[91],"observation":[93],"early":[95,123],"layers":[97],"mainly":[100],"emphasize":[101],"local":[102,142],"patterns,":[103],"though":[104],"limited":[106],"benefits.":[107],"Specifically,":[108],"employ":[110],"plain":[112],"multi-layer":[113],"perceptrons":[114],"(MLP),":[115],"i.e.,":[116],"Spatial":[117],"shift":[118],"MLP":[119],"(S-MLP),":[120],"stages":[124],"feature":[126,167,196],"extraction.":[127,168],"module":[129],"employs":[130],"spatial":[132],"offset":[133],"operation":[134],"facilitate":[136],"communication":[137],"between":[138],"patches,":[139],"encoding":[140],"rich":[141],"patterns":[143],"effectively":[145],"reducing":[146],"computational":[147],"expenses.":[148],"We":[149,169,201],"further":[150],"sparse":[153],"Transformer":[154],"block":[155],"adaptively":[157],"selects":[158],"preserves":[160],"most":[162,187],"valuable":[163],"values":[165],"introduce":[170],"learnable":[172],"top-k":[173],"selection":[174],"operator,":[175],"allowing":[176],"model":[178],"selectively":[180],"retain":[181],"attention":[182],"scores":[183],"contribute":[185],"each":[189],"query":[190],"keyword.":[191],"innovation":[193],"significantly":[194],"enhances":[195],"extraction":[197],"later":[199],"stages.":[200],"conducted":[202],"extensive":[203],"evaluations":[204],"using":[205],"publicly":[206],"available":[207],"datasets,":[208],"experimental":[211],"results":[212],"confirm":[213],"our":[215],"method":[217],"achieves":[218],"state-of-the-art":[219],"performance":[220],"deformable":[222]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
