{"id":"https://openalex.org/W4416251634","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228558","title":"MS-DDNet: Efficient Multi-Scale Dual Decoder Network for Ultrasound Thyroid Nodule Segmentation","display_name":"MS-DDNet: Efficient Multi-Scale Dual Decoder Network for Ultrasound Thyroid Nodule Segmentation","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251634","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228558"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228558","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228558","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5103486673","display_name":"Juan Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juan Wu","raw_affiliation_strings":["Wuhan University of Science and Technology,School of Computer Science and Technology,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University of Science and Technology,School of Computer Science and Technology,Wuhan,China","institution_ids":["https://openalex.org/I43922553"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100361964","display_name":"Jun Liu","orcid":"https://orcid.org/0000-0002-8627-5085"},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Liu","raw_affiliation_strings":["Wuhan University of Science and Technology,School of Computer Science and Technology,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University of Science and Technology,School of Computer Science and Technology,Wuhan,China","institution_ids":["https://openalex.org/I43922553"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055651121","display_name":"Liping Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liping Ye","raw_affiliation_strings":["Wuhan Qingchuan University,School of Computer Science,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan Qingchuan University,School of Computer Science,Wuhan,China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10329","display_name":"Thyroid Cancer Diagnosis and Treatment","score":0.4471000134944916,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10329","display_name":"Thyroid Cancer Diagnosis and Treatment","score":0.4471000134944916,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.1868000030517578,"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/T10862","display_name":"AI in cancer detection","score":0.1678999960422516,"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/segmentation","display_name":"Segmentation","score":0.781000018119812},{"id":"https://openalex.org/keywords/thyroid-nodules","display_name":"Thyroid nodules","score":0.656000018119812},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5673999786376953},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5667999982833862},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5299999713897705},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.460999995470047},{"id":"https://openalex.org/keywords/nodule","display_name":"Nodule (geology)","score":0.41449999809265137}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.781000018119812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7265999913215637},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6980999708175659},{"id":"https://openalex.org/C2779022025","wikidata":"https://www.wikidata.org/wiki/Q53829","display_name":"Thyroid nodules","level":3,"score":0.656000018119812},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5673999786376953},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5667999982833862},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5299999713897705},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C2776731575","wikidata":"https://www.wikidata.org/wiki/Q2916245","display_name":"Nodule (geology)","level":2,"score":0.41449999809265137},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41290000081062317},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4020000100135803},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.39079999923706055},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.3327000141143799},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C526584372","wikidata":"https://www.wikidata.org/wiki/Q16399","display_name":"Thyroid","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228558","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228558","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2027685755","https://openalex.org/W2114866336","https://openalex.org/W2132083787","https://openalex.org/W2412782625","https://openalex.org/W2697546557","https://openalex.org/W2741754476","https://openalex.org/W2787091153","https://openalex.org/W2799217622","https://openalex.org/W2809903694","https://openalex.org/W2824729023","https://openalex.org/W2921406441","https://openalex.org/W2963284331","https://openalex.org/W2964098128","https://openalex.org/W2981413347","https://openalex.org/W2996290406","https://openalex.org/W2999603322","https://openalex.org/W3097065222","https://openalex.org/W3111852437","https://openalex.org/W3138516171","https://openalex.org/W3165252795","https://openalex.org/W3165326420","https://openalex.org/W3168491317","https://openalex.org/W4312084891","https://openalex.org/W4319744372","https://openalex.org/W4390873076"],"related_works":[],"abstract_inverted_index":{"Thyroid":[0,39],"nodules":[1,26,40,56],"are":[2],"common":[3],"nodular":[4],"lesions":[5],"with":[6,51,102],"a":[7,19,63,81,95,122,153],"high":[8],"prevalence":[9],"and":[10,36,44,48,117,160,163,174,187,189],"risk":[11],"of":[12,24,57,115,136],"progression":[13],"to":[14,110,139,167,201],"thyroid":[15,25,90],"cancer,":[16],"which":[17,129],"is":[18,30,130],"life-threatening":[20],"disease.":[21],"Accurate":[22],"segmentation":[23,169,176,198],"from":[27],"ultrasound":[28,65],"images":[29],"crucial":[31],"for":[32,72,89,157],"quantifying":[33],"the":[34,112,133,137,147,172,190],"disease":[35],"evaluating":[37],"treatment.":[38],"exhibit":[41],"irregular":[42],"sizes":[43,59],"shapes,":[45],"blurred":[46],"margins,":[47],"low":[49],"contrast":[50],"surrounding":[52],"tissues.":[53],"Additionally,":[54],"multiple":[55],"varying":[58],"may":[60],"appear":[61],"within":[62],"single":[64],"image.":[66],"These":[67],"factors":[68],"introduce":[69],"significant":[70],"challenges":[71],"accurate":[73],"segmentation.":[74,92],"Therefore,":[75],"in":[76],"this":[77],"paper,":[78],"we":[79,151],"propose":[80,121],"novel":[82],"network":[83],"(MS-DDNet)":[84],"based":[85],"on":[86,182],"encoder-decoder":[87],"architecture":[88],"nodule":[91],"We":[93,120,178],"design":[94,152],"feature":[96],"hybrid":[97],"encoder":[98,138],"that":[99,194],"integrate":[100],"Transformer":[101],"axial":[103],"attention":[104],"mechanism":[105],"into":[106,132],"convolutional":[107],"neural":[108],"networks":[109],"make":[111],"best":[113],"use":[114,164],"local":[116],"global":[118],"information.":[119],"dynamic":[123],"multiscale":[124,141],"context":[125],"awareness":[126],"module":[127],"(DMCA),":[128],"introduced":[131],"final":[134],"stage":[135],"extract":[140],"contextual":[142],"information":[143],"by":[144],"dynamically":[145],"adjusting":[146],"receptive":[148],"field.":[149],"Finally,":[150],"dual":[154],"branch":[155],"decoder":[156],"background":[158,175],"prediction":[159],"foreground":[161,173],"segmentation,":[162],"deep":[165],"supervision":[166],"promote":[168],"before":[170],"fusing":[171],"results.":[177],"evaluate":[179],"our":[180],"method":[181],"two":[183],"public":[184],"datasets":[185],"(TN3K":[186],"DDTI),":[188],"experimental":[191],"results":[192],"show":[193],"MS-DDNet":[195],"demonstrates":[196],"superior":[197],"performance":[199],"compared":[200],"other":[202],"state-of-the-art":[203],"methods.":[204]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-14T00:00:00"}
