{"id":"https://openalex.org/W4415539961","doi":"https://doi.org/10.1145/3746027.3755223","title":"Hierarchical Spatiotemporal Context Aggregation and Speckle-aware Deformable Convolution for Echocardiography Video Segmentation","display_name":"Hierarchical Spatiotemporal Context Aggregation and Speckle-aware Deformable Convolution for Echocardiography Video Segmentation","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415539961","doi":"https://doi.org/10.1145/3746027.3755223"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755223","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755223","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","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/A5114949640","display_name":"Jingxing Guo","orcid":"https://orcid.org/0009-0005-2218-591X"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingxing Guo","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013000901","display_name":"G. H. Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guilian Chen","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yimu Sun","orcid":"https://orcid.org/0009-0002-7011-1729"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yimu Sun","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026540573","display_name":"Huisi Wu","orcid":"https://orcid.org/0000-0002-0399-9089"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huisi Wu","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100662807","display_name":"Jing Qin","orcid":"https://orcid.org/0000-0002-7059-0929"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jing Qin","raw_affiliation_strings":["Hong Kong Polytechnic University, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Hong Kong Polytechnic University, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5114949640"],"corresponding_institution_ids":["https://openalex.org/I180726961"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29882898,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3865","last_page":"3874"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9933000206947327,"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":0.9933000206947327,"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":0.975600004196167,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9624999761581421,"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/segmentation","display_name":"Segmentation","score":0.7282000184059143},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5993000268936157},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.49320000410079956},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4844000041484833},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4803999960422516},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4726000130176544},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.45660001039505005},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4339999854564667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7734000086784363},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7444999814033508},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7282000184059143},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5993000268936157},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5564000010490417},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.49320000410079956},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4844000041484833},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4803999960422516},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4726000130176544},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.45660001039505005},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4339999854564667},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.4327999949455261},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4309000074863434},{"id":"https://openalex.org/C2780757906","wikidata":"https://www.wikidata.org/wiki/Q5276676","display_name":"Dilation (metric space)","level":2,"score":0.4122999906539917},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.37290000915527344},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.33059999346733093},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2572000026702881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3755223","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755223","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4206905956","display_name":null,"funder_award_id":"2024A1515011946","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"},{"id":"https://openalex.org/G6694410858","display_name":null,"funder_award_id":"62273241","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2048309569","https://openalex.org/W2982468271","https://openalex.org/W3013692475","https://openalex.org/W3036107862","https://openalex.org/W3089717863","https://openalex.org/W3089778897","https://openalex.org/W3173365702","https://openalex.org/W3209901427","https://openalex.org/W4214493665","https://openalex.org/W4214701508","https://openalex.org/W4224325106","https://openalex.org/W4308769137","https://openalex.org/W4312349930","https://openalex.org/W4312396403","https://openalex.org/W4312560592","https://openalex.org/W4312614039","https://openalex.org/W4312950730","https://openalex.org/W4390874575"],"related_works":[],"abstract_inverted_index":{"Automatic":[0],"segmentation":[1,50],"of":[2,25,33,182],"echocardiography":[3],"videos":[4],"is":[5,17],"crucial":[6],"for":[7],"computer-aided":[8],"cardiovascular":[9],"function":[10],"assessment":[11],"in":[12,109,180],"clinical":[13],"practice.":[14],"However,":[15],"it":[16,123],"a":[18,46,67,119],"challenging":[19],"task":[20],"owing":[21],"to":[22,52,79,124,145,155],"the":[23,29,100,114,127,143,167],"existence":[24],"massive":[26],"speckle":[27,105,138],"noise,":[28],"large":[30],"shape":[31],"variations":[32],"heart":[34],"structures":[35],"between":[36],"frames,":[37],"and":[38,73,83,130,160,169,185],"limited":[39],"annotations.":[40],"In":[41],"this":[42],"paper,":[43],"we":[44,65,112],"propose":[45,66],"novel":[47],"semi-supervised":[48],"video":[49],"model":[51],"comprehensively":[53],"meet":[54],"these":[55,92],"challenges.":[56],"The":[57],"proposed":[58],"approach":[59],"has":[60],"two":[61],"key":[62],"techniques.":[63],"First,":[64],"dual-stream":[68],"architecture":[69],"that":[70,173],"processes":[71],"spatial":[72],"temporal":[74],"features":[75,94,97],"through":[76],"separate":[77],"pathways":[78],"capture":[80,156],"structural":[81],"details":[82],"motion":[84],"patterns,":[85],"then":[86],"enhances":[87],"spatiotemporal":[88],"representations":[89],"by":[90],"interacting":[91],"decomposed":[93],"with":[95],"query":[96],"generated":[98],"from":[99,118],"original":[101],"input.":[102],"Second,":[103],"as":[104],"noise":[106,139],"primarily":[107],"concentrates":[108],"high-frequency":[110,161],"regions,":[111],"extend":[113],"traditional":[115],"dilated":[116],"convolution":[117,131],"frequency":[120,137,149],"perspective,":[121],"enabling":[122],"adaptively":[125],"adjust":[126],"dilation":[128],"rate":[129],"kernel":[132],"weights":[133],"based":[134],"on":[135,147,166],"high":[136],"information.":[140],"This":[141],"enables":[142],"network":[144],"focus":[146],"specific":[148],"bands,":[150],"thereby":[151],"enhancing":[152],"its":[153],"ability":[154],"both":[157,183],"low-frequency":[158],"context":[159],"local":[162],"details.":[163],"Extensive":[164],"experiments":[165],"CAMUS":[168],"EchoNet-Dynamic":[170],"datasets":[171],"demonstrate":[172],"our":[174],"method":[175],"outperforms":[176],"existing":[177],"state-of-the-art":[178],"methods":[179],"terms":[181],"accuracy":[184],"inference":[186],"speed.":[187],"Codes":[188],"are":[189],"available":[190],"at":[191],"https://github.com/guojx2255/HSCA-SDC.":[192]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-25T00:00:00"}