{"id":"https://openalex.org/W4210563666","doi":"https://doi.org/10.1109/jbhi.2022.3146013","title":"Cascaded Triplanar Autoencoder M-Net for Fully Automatic Segmentation of Left Ventricle Myocardial Scar From Three-Dimensional Late Gadolinium-Enhanced MR Images","display_name":"Cascaded Triplanar Autoencoder M-Net for Fully Automatic Segmentation of Left Ventricle Myocardial Scar From Three-Dimensional Late Gadolinium-Enhanced MR Images","publication_year":2022,"publication_date":"2022-01-25","ids":{"openalex":"https://openalex.org/W4210563666","doi":"https://doi.org/10.1109/jbhi.2022.3146013","pmid":"https://pubmed.ncbi.nlm.nih.gov/35077377"},"language":"en","primary_location":{"id":"doi:10.1109/jbhi.2022.3146013","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2022.3146013","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"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 Journal of Biomedical and Health Informatics","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":null,"display_name":"Mingquan Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Mingquan Lin","raw_affiliation_strings":["Department of Electrical Engineering, City University of Hong Kong, SAR, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, City University of Hong Kong, SAR, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Mingjie Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Mingjie Jiang","raw_affiliation_strings":["Department of Electrical Engineering, City University of Hong Kong, SAR, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, City University of Hong Kong, SAR, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Mingbo Zhao","orcid":"https://orcid.org/0000-0003-0381-4360"},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingbo Zhao","raw_affiliation_strings":["School of Information Science and Technology, Donghua University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Donghua University, Shanghai, China","institution_ids":["https://openalex.org/I181326427"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Eranga Ukwatta","orcid":"https://orcid.org/0000-0003-0180-4716"},"institutions":[{"id":"https://openalex.org/I79817857","display_name":"University of Guelph","ror":"https://ror.org/01r7awg59","country_code":"CA","type":"education","lineage":["https://openalex.org/I79817857"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Eranga Ukwatta","raw_affiliation_strings":["University of Guelph, Guelph, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"University of Guelph, Guelph, Ontario, Canada","institution_ids":["https://openalex.org/I79817857"]}]},{"author_position":"middle","author":{"id":null,"display_name":"James A. White","orcid":null},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]},{"id":"https://openalex.org/I56478863","display_name":"Libin Cardiovascular Institute of Alberta","ror":"https://ror.org/02qthww36","country_code":"CA","type":"facility","lineage":["https://openalex.org/I56478863"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"James A. White","raw_affiliation_strings":["Stephenson Cardiac Imaging Centre at the Libin Cardiovascular Institute, University of Calgary, Calgary, Alberta, Canada"],"affiliations":[{"raw_affiliation_string":"Stephenson Cardiac Imaging Centre at the Libin Cardiovascular Institute, University of Calgary, Calgary, Alberta, Canada","institution_ids":["https://openalex.org/I56478863","https://openalex.org/I168635309"]}]},{"author_position":"last","author":{"id":null,"display_name":"Bernard Chiu","orcid":"https://orcid.org/0000-0001-5237-2410"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Bernard Chiu","raw_affiliation_strings":["Department of Electrical Engineering, City University of Hong Kong, SAR, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, City University of Hong Kong, SAR, China","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I168719708"],"apc_list":null,"apc_paid":null,"fwci":3.1769,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.91883245,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"26","issue":"6","first_page":"2582","last_page":"2593"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.698199987411499,"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"}},"topics":[{"id":"https://openalex.org/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.698199987411499,"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":0.039500001817941666,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.023900000378489494,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.9157000184059143},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7232000231742859},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6815999746322632},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5730999708175659},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5723000168800354},{"id":"https://openalex.org/keywords/ventricle","display_name":"Ventricle","score":0.5389999747276306},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5206000208854675},{"id":"https://openalex.org/keywords/sagittal-plane","display_name":"Sagittal plane","score":0.4498000144958496}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9157000184059143},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7674000263214111},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7232000231742859},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6815999746322632},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6136000156402588},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5730999708175659},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5723000168800354},{"id":"https://openalex.org/C2778921608","wikidata":"https://www.wikidata.org/wiki/Q2002035","display_name":"Ventricle","level":2,"score":0.5389999747276306},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5206000208854675},{"id":"https://openalex.org/C178910020","wikidata":"https://www.wikidata.org/wiki/Q2211994","display_name":"Sagittal plane","level":2,"score":0.4498000144958496},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4390999972820282},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.4214000105857849},{"id":"https://openalex.org/C13483470","wikidata":"https://www.wikidata.org/wiki/Q1470152","display_name":"Coronal plane","level":2,"score":0.41620001196861267},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.38260000944137573},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.3815999925136566},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.38119998574256897},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C2776008845","wikidata":"https://www.wikidata.org/wiki/Q5038325","display_name":"Cardiac magnetic resonance imaging","level":3,"score":0.2964000105857849},{"id":"https://openalex.org/C3019831412","wikidata":"https://www.wikidata.org/wiki/Q5778278","display_name":"Fully automatic","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C180400216","wikidata":"https://www.wikidata.org/wiki/Q7605197","display_name":"Steady-state free precession imaging","level":3,"score":0.2549999952316284}],"mesh":[{"descriptor_ui":"D002921","descriptor_name":"Cicatrix","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D002921","descriptor_name":"Cicatrix","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D002921","descriptor_name":"Cicatrix","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D002921","descriptor_name":"Cicatrix","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D002921","descriptor_name":"Cicatrix","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D002921","descriptor_name":"Cicatrix","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D005682","descriptor_name":"Gadolinium","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D005682","descriptor_name":"Gadolinium","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D005682","descriptor_name":"Gadolinium","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006352","descriptor_name":"Heart Ventricles","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D006352","descriptor_name":"Heart Ventricles","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D006352","descriptor_name":"Heart Ventricles","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"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":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D009206","descriptor_name":"Myocardium","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D009206","descriptor_name":"Myocardium","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D009206","descriptor_name":"Myocardium","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/jbhi.2022.3146013","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2022.3146013","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"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 Journal of Biomedical and Health Informatics","raw_type":"journal-article"},{"id":"pmid:35077377","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35077377","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 journal of biomedical and health informatics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2734571567","display_name":null,"funder_award_id":"7005226","funder_id":"https://openalex.org/F4320309893","funder_display_name":"City University of Hong Kong"},{"id":"https://openalex.org/G853187529","display_name":null,"funder_award_id":"7005441","funder_id":"https://openalex.org/F4320309893","funder_display_name":"City University of Hong Kong"}],"funders":[{"id":"https://openalex.org/F4320309893","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W845365781","https://openalex.org/W1480256845","https://openalex.org/W1570404261","https://openalex.org/W1767244069","https://openalex.org/W1901129140","https://openalex.org/W1966190213","https://openalex.org/W1969085832","https://openalex.org/W1978087315","https://openalex.org/W1979226691","https://openalex.org/W1986622667","https://openalex.org/W1987015763","https://openalex.org/W1988308001","https://openalex.org/W1988707821","https://openalex.org/W1989443948","https://openalex.org/W1992763477","https://openalex.org/W2011548497","https://openalex.org/W2019400046","https://openalex.org/W2025362214","https://openalex.org/W2043037509","https://openalex.org/W2080971686","https://openalex.org/W2083823350","https://openalex.org/W2104095591","https://openalex.org/W2110408320","https://openalex.org/W2111848616","https://openalex.org/W2126171519","https://openalex.org/W2127890285","https://openalex.org/W2149632605","https://openalex.org/W2149710008","https://openalex.org/W2153322653","https://openalex.org/W2171177394","https://openalex.org/W2194775991","https://openalex.org/W2201213193","https://openalex.org/W2546752250","https://openalex.org/W2613255783","https://openalex.org/W2782364420","https://openalex.org/W2900827846","https://openalex.org/W2912963483","https://openalex.org/W2964185501","https://openalex.org/W2979448322","https://openalex.org/W2983823075","https://openalex.org/W2988868890","https://openalex.org/W2996131899","https://openalex.org/W3000054715","https://openalex.org/W3014974815","https://openalex.org/W3090886579","https://openalex.org/W3132190499","https://openalex.org/W3172614365","https://openalex.org/W4241501232","https://openalex.org/W6600284362","https://openalex.org/W6608141721","https://openalex.org/W6636871134","https://openalex.org/W6684191040","https://openalex.org/W6684372118","https://openalex.org/W6684665197","https://openalex.org/W6739365718","https://openalex.org/W6786725005"],"related_works":[],"abstract_inverted_index":{"While":[0],"three-dimensional":[1],"(3D)":[2],"late":[3],"gadolinium-enhanced":[4],"(LGE)":[5],"magnetic":[6],"resonance":[7],"(MR)":[8],"imaging":[9,250],"provides":[10,139,169],"good":[11],"conspicuity":[12],"of":[13,32,97,129,172,233,248],"small":[14],"myocardial":[15,57],"lesions":[16],"with":[17,103],"short":[18],"acquisition":[19],"time,":[20],"it":[21],"poses":[22],"a":[23,29,42,161],"challenge":[24],"for":[25,176,196],"image":[26],"analysis":[27],"as":[28],"large":[30,228],"number":[31],"axial":[33],"images":[34],"are":[35,184],"required":[36,195],"to":[37,55,67,142,165,190,198],"be":[38],"segmented.":[39],"We":[40],"developed":[41],"fully":[43],"automatic":[44],"convolutional":[45],"neural":[46],"network":[47],"(CNN)":[48],"called":[49],"cascaded":[50,66],"triplanar":[51],"autoencoder":[52,87,159],"M-Net":[53],"(CTAEM-Net)":[54],"segment":[56,68,199],"scar":[58,77,180,204],"from":[59,158],"3D":[60,99],"LGE":[61,100],"MRI.":[62],"Two":[63],"sub-networks":[64],"were":[65],"the":[69,76,79,91,98,104,130,185,203,234],"left":[70],"ventricle":[71],"(LV)":[72],"myocardium":[73,178,201],"and":[74,94,121,132,145,174,179,202,209,219],"then":[75],"within":[78],"pre-segmented":[80],"LV":[81,177,200],"myocardium.":[82],"Each":[83],"sub-network":[84],"contains":[85],"three":[86,113],"M-Nets":[88],"(AEM-Nets)":[89],"segmenting":[90],"axial,":[92],"sagittal":[93],"coronal":[95],"slices":[96],"MR":[101,214],"image,":[102],"final":[105],"segmentation":[106,152],"determined":[107],"by":[108,154,222,239],"voting.":[109],"The":[110,124,167,193,217,231],"AEM-Net":[111],"integrates":[112],"features:":[114],"(1)":[115],"multi-scale":[116,125],"inputs,":[117],"(2)":[118],"deep":[119],"supervision":[120,138,141],"(3)":[122],"multi-tasking.":[123],"inputs":[126],"allow":[127],"consideration":[128],"global":[131],"local":[133],"features":[134],"in":[135,243],"segmentation.":[136,166],"Deep":[137],"direct":[140],"deeper":[143],"layers":[144],"facilitates":[146],"CNN":[147],"convergence.":[148],"Multi-task":[149],"learning":[150],"reduces":[151],"overfitting":[153],"acquiring":[155],"additional":[156],"information":[157],"reconstruction,":[160],"task":[162],"closely":[163],"related":[164],"framework":[168,235],"an":[170],"accuracy":[171,218],"86.43%":[173],"90.18%":[175],"segmentation,":[181],"respectively,":[182],"which":[183],"highest":[186],"among":[187],"existing":[188],"methods":[189],"our":[191],"knowledge.":[192],"time":[194],"CTAEM-Net":[197,223],"was":[205,236],"49.72":[206],"\u00b1":[207,211],"9.69s":[208],"120.25":[210],"23.18s":[212],"per":[213],"volume,":[215],"respectively.":[216],"efficiency":[220],"afforded":[221],"will":[224],"make":[225],"possible":[226],"future":[227],"population":[229],"studies.":[230],"generalizability":[232],"also":[237],"demonstrated":[238],"its":[240],"competitive":[241],"performance":[242],"two":[244],"publicly":[245],"available":[246],"datasets":[247],"different":[249],"modalities.":[251]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":5}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2022-02-08T00:00:00"}