{"id":"https://openalex.org/W4386233974","doi":"https://doi.org/10.1109/tip.2023.3304518","title":"MDF-Net: A Multi-Scale Dynamic Fusion Network for Breast Tumor Segmentation of Ultrasound Images","display_name":"MDF-Net: A Multi-Scale Dynamic Fusion Network for Breast Tumor Segmentation of Ultrasound Images","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4386233974","doi":"https://doi.org/10.1109/tip.2023.3304518","pmid":"https://pubmed.ncbi.nlm.nih.gov/37639409"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2023.3304518","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2023.3304518","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5053836457","display_name":"Wenbo Qi","orcid":"https://orcid.org/0000-0001-7259-9751"},"institutions":[{"id":"https://openalex.org/I4210086892","display_name":"Education University of Hong Kong","ror":"https://ror.org/000t0f062","country_code":"HK","type":"education","lineage":["https://openalex.org/I4210086892"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Wenbo Qi","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, SAR, China","Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, P.R.C"],"raw_orcid":"https://orcid.org/0000-0001-7259-9751","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, SAR, China","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, P.R.C","institution_ids":["https://openalex.org/I4210086892","https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101830959","display_name":"H. C. Wu","orcid":"https://orcid.org/0000-0002-4555-3675"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]},{"id":"https://openalex.org/I4210148693","display_name":"Metropolitan University","ror":"https://ror.org/04hdrrs71","country_code":"BD","type":"education","lineage":["https://openalex.org/I4210148693"]}],"countries":["BD","HK"],"is_corresponding":false,"raw_author_name":"H. C. Wu","raw_affiliation_strings":["Department of Electronic Engineering and Computer Science, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, SAR, China","Department of Electronic Engineering and Computer Science, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, P.R.C"],"raw_orcid":"https://orcid.org/0000-0002-4555-3675","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering and Computer Science, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, SAR, China","institution_ids":["https://openalex.org/I200769079","https://openalex.org/I4210148693"]},{"raw_affiliation_string":"Department of Electronic Engineering and Computer Science, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, P.R.C","institution_ids":["https://openalex.org/I200769079","https://openalex.org/I4210148693"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057049452","display_name":"S. C. Chan","orcid":"https://orcid.org/0000-0001-7212-4182"},"institutions":[{"id":"https://openalex.org/I4210086892","display_name":"Education University of Hong Kong","ror":"https://ror.org/000t0f062","country_code":"HK","type":"education","lineage":["https://openalex.org/I4210086892"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"S. C. Chan","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, SAR, China","Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, P.R.C"],"raw_orcid":"https://orcid.org/0000-0001-7212-4182","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, SAR, China","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, P.R.C","institution_ids":["https://openalex.org/I4210086892","https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.2515,"has_fulltext":false,"cited_by_count":55,"citation_normalized_percentile":{"value":0.97937738,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"32","issue":null,"first_page":"4842","last_page":"4855"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9987000226974487,"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":0.9979000091552734,"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":0.7747952938079834},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7440897822380066},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6993181705474854},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.6689900755882263},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6638021469116211},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5700469017028809},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5200836062431335},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48529332876205444},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4562888741493225},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41533878445625305},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.41155678033828735},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.3906967043876648},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3891003131866455},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.14480531215667725}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7747952938079834},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7440897822380066},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6993181705474854},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.6689900755882263},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6638021469116211},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5700469017028809},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5200836062431335},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48529332876205444},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4562888741493225},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41533878445625305},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.41155678033828735},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.3906967043876648},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3891003131866455},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.14480531215667725},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001943","descriptor_name":"Breast Neoplasms","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D001943","descriptor_name":"Breast Neoplasms","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D001943","descriptor_name":"Breast Neoplasms","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009844","descriptor_name":"Oligosaccharides","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014463","descriptor_name":"Ultrasonography","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014463","descriptor_name":"Ultrasonography","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014463","descriptor_name":"Ultrasonography","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016477","descriptor_name":"Artifacts","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016477","descriptor_name":"Artifacts","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016477","descriptor_name":"Artifacts","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2023.3304518","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2023.3304518","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:37639409","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37639409","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6299999952316284,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":78,"referenced_works":["https://openalex.org/W36055984","https://openalex.org/W1522301498","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1963181001","https://openalex.org/W1969013163","https://openalex.org/W1990318218","https://openalex.org/W2019310665","https://openalex.org/W2028672930","https://openalex.org/W2032492895","https://openalex.org/W2069125771","https://openalex.org/W2070252766","https://openalex.org/W2079458462","https://openalex.org/W2085279201","https://openalex.org/W2107920575","https://openalex.org/W2116750805","https://openalex.org/W2119249988","https://openalex.org/W2120041316","https://openalex.org/W2123139184","https://openalex.org/W2140067226","https://openalex.org/W2168894214","https://openalex.org/W2189708919","https://openalex.org/W2429450644","https://openalex.org/W2493683088","https://openalex.org/W2521537533","https://openalex.org/W2571079985","https://openalex.org/W2577153738","https://openalex.org/W2594373254","https://openalex.org/W2744692634","https://openalex.org/W2798122215","https://openalex.org/W2804525729","https://openalex.org/W2810846975","https://openalex.org/W2883547127","https://openalex.org/W2884436604","https://openalex.org/W2889646458","https://openalex.org/W2897943478","https://openalex.org/W2899332989","https://openalex.org/W2914162368","https://openalex.org/W2915825499","https://openalex.org/W2922518371","https://openalex.org/W2946044075","https://openalex.org/W2962957359","https://openalex.org/W2963104294","https://openalex.org/W2963351448","https://openalex.org/W2979878425","https://openalex.org/W2991372685","https://openalex.org/W3002592716","https://openalex.org/W3011075597","https://openalex.org/W3014974815","https://openalex.org/W3015788359","https://openalex.org/W3016758101","https://openalex.org/W3016950572","https://openalex.org/W3019643114","https://openalex.org/W3021454079","https://openalex.org/W3024640380","https://openalex.org/W3037414627","https://openalex.org/W3112701542","https://openalex.org/W3123982987","https://openalex.org/W3127751679","https://openalex.org/W3138516171","https://openalex.org/W3160284783","https://openalex.org/W3176271734","https://openalex.org/W3187515607","https://openalex.org/W4200134793","https://openalex.org/W4319300975","https://openalex.org/W4389961130","https://openalex.org/W6631190155","https://openalex.org/W6639824700","https://openalex.org/W6677695216","https://openalex.org/W6679383148","https://openalex.org/W6684665197","https://openalex.org/W6750469568","https://openalex.org/W6753182481","https://openalex.org/W6755348854","https://openalex.org/W6766433800","https://openalex.org/W6790275670","https://openalex.org/W6795435739","https://openalex.org/W6846395082"],"related_works":["https://openalex.org/W2970216048","https://openalex.org/W1669643531","https://openalex.org/W2005437358","https://openalex.org/W2517104666","https://openalex.org/W1967061043","https://openalex.org/W2549936415","https://openalex.org/W1982826852","https://openalex.org/W2566648451","https://openalex.org/W2384989255","https://openalex.org/W2045480779"],"abstract_inverted_index":{"Breast":[0],"tumor":[1,36,53,189,236,245],"segmentation":[2,17,185,259,276,300],"of":[3,9,27,117,154,263,293],"ultrasound":[4,52],"images":[5],"provides":[6],"valuable":[7],"information":[8,186,200],"tumors":[10],"for":[11,50,65,75],"early":[12],"detection":[13],"and":[14,31,38,69,81,88,131,164,191,196,218,240],"diagnosis.":[15],"Accurate":[16],"is":[18,93,124,148,232,251,267],"challenging":[19,298],"due":[20],"to":[21,103,126,150,182,235,254,304],"low":[22],"image":[23,299],"contrast":[24],"between":[25,107],"areas":[26],"interest;":[28],"speckle":[29,135,238],"noises,":[30],"large":[32],"inter-subject":[33,82,198],"variations":[34],"in":[35,122,257,296],"shape":[37,190],"size.":[39],"This":[40,289],"paper":[41],"proposes":[42],"a":[43,57,62,70,98,138,151,177,280],"novel":[44],"Multi-scale":[45],"Dynamic":[46],"Fusion":[47],"Network":[48],"(MDF-Net)":[49],"breast":[51],"segmentation.":[54],"It":[55,278],"employs":[56],"two-stage":[58],"end-to-end":[59],"architecture":[60],"with":[61,97,286,302],"trunk":[63,91],"sub-network":[64,74,175],"multiscale":[66],"feature":[67,86],"selection":[68],"structurally":[71,178],"optimized":[72,179],"refinement":[73,165,174],"mitigating":[76,258],"impairments":[77],"such":[78],"as":[79],"noise":[80,136,239],"variation":[83,199],"via":[84,137],"better":[85,216],"exploration":[87],"fusion.":[89],"The":[90,173,261],"network":[92,231,266],"extended":[94],"from":[95,134,206],"UNet++":[96],"simplified":[99],"skip":[100],"pathway":[101],"structure":[102],"connect":[104],"the":[105,119,145,155,161,212,264,271,291],"features":[106,130],"adjacent":[108],"scales.":[109,203],"Moreover,":[110],"deep":[111],"supervision":[112],"at":[113,118,171,193,201],"all":[114],"scales,":[115],"instead":[116],"finest":[120],"scale":[121],"UNet++,":[123],"proposed":[125,213,230,253,265],"extract":[127],"more":[128,233],"discriminative":[129],"mitigate":[132],"errors":[133],"hybrid":[139],"loss":[140,152],"function.":[141],"Unlike":[142],"previous":[143],"works,":[144],"first":[146],"stage":[147,157],"linked":[149],"function":[153],"second":[156],"so":[158],"that":[159,211,228],"both":[160],"preliminary":[162,184],"segmentations":[163],"subnetworks":[166],"can":[167],"be":[168],"refined":[169],"together":[170],"training.":[172],"utilizes":[176],"MDF":[180],"mechanism":[181],"integrate":[183],"(capturing":[187],"general":[188],"size)":[192],"coarse":[194],"scales":[195],"explores":[197],"finer":[202],"Experimental":[204],"results":[205],"two":[207],"public":[208],"datasets":[209],"show":[210],"method":[214],"achieves":[215],"Dice":[217],"other":[219,297],"scores":[220],"over":[221],"state-of-the-art":[222,281],"methods.":[223],"Qualitative":[224],"analysis":[225],"also":[226,252,268],"indicates":[227,290],"our":[229,294],"robust":[234],"size/shapes,":[237],"heavy":[241],"posterior":[242],"shadows":[243],"along":[244],"boundaries.":[246],"An":[247],"optional":[248],"post-processing":[249],"step":[250],"facilitate":[255],"users":[256],"artifacts.":[260],"efficiency":[262],"illustrated":[269],"on":[270,284],"\"Electron":[272],"Microscopy":[273],"neural":[274],"structures":[275],"dataset\".":[277],"outperforms":[279],"algorithm":[282],"based":[283],"UNet-2022":[285],"simpler":[287],"settings.":[288],"advantages":[292],"MDF-Nets":[295],"tasks":[301],"small":[303],"medium":[305],"data":[306],"sizes.":[307]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":25},{"year":2024,"cited_by_count":17},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}