深度学习打造可泛化的心脏MRI自动分析框架 A generalizable deep learning system for cardiac MRI

NatureBiomedicalEngineering

naturebiomedicalengineering

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/10.1038/s41551-026-01637-3

AgeneralizabledeeplearningsystemforcardiacMRI

Received:16October2024

Accepted:23February2026

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forupdates

RohanShad1,CyrilZakka2,DhamanpreetKaur2,MrudangMathur2,

RobynFong2,JosephCho2,RossWarrenFilice3,JohnMongan4,

KimberlyKallianos4,NishithKhandwala5,DavidEng5,MatthewLeipzig2,

WalterRWitschey6,AlejandrodeFeria7,VictorAFerrari7,EuanAAshley8

...,MichaelA.Acker1,CurtisLanglotz9&WilliamHiesinger2

CardiacMRIallowsforacomprehensiveassessmentofmyocardial

structure,functionandtissuecharacteristics.Herewedescribea

foundationalvisionsystemforcardiacMRI,capableofrepresentingthe

breadthofhumancardiovasculardiseaseandhealth.Ourdeep-learning

modelistrainedviaself-supervisedcontrastivelearning,inwhichvisual

conceptsincine-sequencecardiacMRIscansarelearnedfromtheraw

textoftheaccompanyingradiologyreports.Wetrainandevaluateour

modelondatafromfourlargeacademicclinicalinstitutionsintheUnited

States.Weadditionallyshowcasetheperformanceofourmodelson

theUKBioBankandtwoadditionalpubliclyavailableexternaldatasets.

Weexploreemergentcapabilitiesofoursystemanddemonstrate

remarkableperformanceacrossarangeoftasks,includingtheproblem

ofleft-ventricularejectionfractionregressionandthediagnosisof39

differentconditionssuchascardiacamyloidosisandhypertrophiccar-

diomyopathy.Weshowthatourdeep-learningsystemiscapableofnot

onlycontextualizingthestaggeringcomplexityofhumancardiovascular

diseasebutcanbedirectedtowardsclinicalproblemsofinterest,yielding

impressive,clinical-gradediagnosticaccuracywithafractionofthetrainingdatatypicallyrequiredforsuchtasks.

Offeringunparalleleddiagnosticclarity,cardiacmagneticresonanceimaging(CMRorcardiacMRI)isthereferencestandardforassessingcardiacanatomyandfunction

1

,

2

.Dependingonthetechniqueused,itenablesclinicianstocapturevideographicsequencesofcardiacandvalvularmotion,quantifyscarringortissueoedemawithinthecardiacmusculature,andidentifyregionsofpoorperfusion—allwithoutexposuretoionizingradiation

2

.Despitethiswealthofdataavailable,deep-learningsystemscapableoflearninghigh-quality

representationsofhumancardiacdiseasefromCMRhavenotyetbeendescribed.

Deeplearninghasshownincrediblepromiseinthediagnosisofcardiovasculardiseasewithelectrocardiography(EKG),retinalscansandechocardiography

3

–

7

.Traditionally,thesenetworksaretrainedtodetectahandfulofpre-definedandcurated‘disease’conditionsinthebackgroundofnegative‘normal’cases.Whilesuperficiallysuc-cessful,manyofthesesystemsfailwhentestedonreal-worldclinical

1DivisionofCardiovascularSurgery,DepartmentofSurgery,UniversityofPennsylvania,Philadelphia,PA,USA.2DepartmentofCardiothoracicSurgery,StanfordUniversity,Stanford,CA,USA.3DepartmentofRadiology,MedstarGeorgetownUniversityHospital,Washington,DC,USA.4Department

ofRadiologyandBiomedicalImaging,UniversityofCalifornia,SanFrancisco,CA,USA.5BunkerhillHealth,SanFrancisco,CA,USA.6Department

ofRadiology,UniversityofPennsylvania,Philadelphia,PA,USA.7DivisionofCardiovascularMedicine,DepartmentofMedicine,Universityof

Pennsylvania,Philadelphia,PA,USA.8DivisionofCardiovascularMedicine,DepartmentofMedicine,Genetics,andBiomedicalDataScience,

StanfordUniversity,Stanford,CA,USA.9DepartmentofRadiology,Medicine,andBiomedicalDataScience,StanfordUniversity,Stanford,CA,USA.

e-mail:

rohan.shad@

;

willhies@

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/10.1038/s41551-026-01637-3

NatureBiomedicalEngineering

datathatareoftenheterogeneouswithnumerousconcomitantabnor-malfindings

8

.Patientswithinheritedcardiomyopathies,forexam-ple,maypresentwithseverevalvulardisease;thosewithevidenceofventricularthrombus,ontheotherhand,mayhavesevereheartfailurefromremoteischaemicinsults.Withthetraditionalsuperviseddeep-learningapproach,itremainschallengingtocontextualizethisdiversityofdiseasepresentationfromCMRscans,withparameterslearnedforoneproblemrarelygeneralizingtoothers

9

.Thesesystems,therefore,mustbere-trainedfromscratchforeverynewclinicaltaskofinterest,requiringthousandsoftrainingexampleseachtime.Unlikehumanclinicians,deep-learningmodelsdonothaveabaselinefoun-dationofclinicalandpathophysiologicalunderstandingoverwhichlearningspecifictaskscanbeaccelerated.Ultimately,thishasrestrictedresearchinthefieldtotasksthateitherautomateobjectivestructuralmeasurementsortodiseasesthataresimplymoreprevalent

10

.

Herewedescribeatransformer-basedvisionsystemthatlearnscomplexpathophysiologicalvisualrepresentationsfromalargemulti-institutionaldatasetof19,041CMRscans,guidedbynaturallanguagesupervisionfromthetextreportsaccompanyingeachCMRstudy.Weusealargelanguagemodeltohelp‘teach’avisionnetworktogeneratemeaningfullow-dimensionalrepresentationsofCMRstudies,byshowingexamplesofhowradiologistsdescribewhattheyseewhiledraftingtheirreports.Wedescribehowthesemodelsgrouptogetherpatientswithsimilarpathophysiologicalandevensocio-demographiccharacteristics,withnoexplicitsupervisionforthesetasks.Wefinetunethisdeep-learningsystemonsmallerexpert-labelleddatasetsforarangeofclinicallyrelevanttasksrangingfromleft-ventricularejectionfractionestimationtothedetectionof39differentcardiovas-cularconditions.Wevalidateoursystemonmultipleexternaldatasetsfromdistinctgeographicalsitesandhealthsystems.Theresultisageneralizableanddata-efficientCMRdeep-learningsystemcapableofrepresentingthebreadthofcardiovasculardiseaseandhealth.Figure

1

detailsanoverviewoftheproject.

CMRimagingdata

Theinputstoournetworksaresteady-statefreeprecession(SSFP)cinesequencestakenalongmultipledifferentcardiacviewplanes.Typically,SSFPsequencesarecharacterizedbyahighbloodsignalintensity,withlowerintensitymyocardialsignal

11

,

12

.Thistechniqueallowsfortheacquisitionofhigh-contrastdynamic-motionscansoftheheartduringabreathholdtimedwithEKGgatingtocaptureimagesbetweenheartbeats.Thesecine-sequencevideosarecapturedalongvariousspatialviewplanesasdescribedbelow.Ourdataaresourcedfromaheterogeneoussamplepopulationwithsubstantialvariabilityinacquisitionandscannervendors,withtrainingdatasourcedfromStanfordMedicine,MedStarandUCSF.AdditionaldatafromtheUKBioBank,KaggleGrandDataChallenge,AutomatedCardiacDiagnosisChallenge(ACDC)datasetandtheUniversityofPennsylvaniaweresourcedtorigorouslyassessmodelgeneralizability.Specificdescrip-tionsofeachcohortareprovidedinMethodsandSupplementaryFig.1.

Differentcardiacstructuresmaybevisualizedbetterinoneviewplanethaninanother.Asaresult,findingsindicativeofpathologymayonlybevisibleinaspecificMRIviewplane.Thisisconceptuallyanalogoustothechallengesfacedindeeplearningforhistopathology,whereonlyafractionofthebiopsiedtissuemightcontainfindingsdiagnosticofdisease

13

.Welimitourselvestoshort-axisstacks(SAX),four-chamber(4CH),three-chamber(3CH)andtwo-chamber(2CH)viewsforthisworkasthesearemostconsistentlycapturedirrespectiveofquerypathology.DemographicdataavailableforcertainsubsetsofthedataaredetailedinSupplementaryTable1.

Pre-trainingframeworkandevaluationoflow-dimensionalrepresentations

Thetraditionalapproachfordeeplearningwithcardiovascularimaginghasbeentoassignsomelabeltoeachimagingscanviatediousmanual

annotation.Large-scalelabellingoftrainingdatarequiresextensivedomainknowledgeandclinicalexpertise,butultimatelyconstrainsthemodelstofeaturesexplicitlylabelledinthedataset.Modelstrainedthiswayrequirethousandsofexamplesforadequateperformanceandareunabletoaccountforfindingsoutsidethescopeofwhattheyweretrainedtoidentify.Recentadvancesinself-superviseddeeplearninghaveshownpromiseinreducingthisrelianceonvastquantitiesofexpert-labelleddataacrossawiderangeofmodalitiesrangingfrompathologyslidestochestX-rays

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–

21

.Aframeworkwhereinunstruc-turedtextcouldbeusedasamethodofself-supervisionforachestX-raydeep-learningsystemhasbeendescribed

16

.Inthismethodofcontrastivelearning,twoneuralnetworksareusedtoproduceapairoflow-dimensionalrepresentationsforeachpairofcontextuallyrelatedinputsfromtwoseparatemodalities

22

.WeextendtheseconceptstothespatiotemporalandmultiviewproblemofcardiacMRI.Duringthepre-trainingprocess,thenetworksaretrainedtomatchtruepairsoftextreportandMRIscansbyoptimizingforacontrastiveobjec-tive

22

.Sincethereleaseofourinitialpreprint,othershavereportedpromisingresultsusingsimilarcontrastive-learningmethodstotraingeneralizable‘foundationmodels’forcomputedtomography(CT)andechocardiography

23

–

25

.

Visualfeaturesofaparticulardiseaseareiterativelyassociatedwithtextualcues,withatrainingprocessguidedbyarichunstruc-tureddescriptionofdiseasecreatedbycliniciansaspartofroutineclinicalworkflow.Weuseanimplementationofamultiscalevisiontransformer(mViT)fortheCMRvisionencoderandabidirectionalencoderrepresentationsfromtransformers(BERT)texttransformerfortheMRIreportencoder

26

,

27

.Wepre-trainandvalidateon14,073cardiacMRIscans(12,707uniquepatients)fromStanford,UCSFandMedStar.SpecificimplementationdetailsaredescribedinMethods.

Weplottheembeddingsgeneratedbythevisionencoderonthevalidationsetusingstandarddimensionalreductionalgorithms(Meth-ods),demonstratingtheprogressiveemergenceoflocalandglobalstructureinthetwo-dimensional(2D)projectionsoftheseembeddingsastrainingprogresses(Fig.

1

).Oncetrainingiscomplete,thenetworkisfrozenanddeployedontoapubliclyavailableexternaldatasetofcine-CMRsequencessourcedfromFrance(ACDCdataset)

28

.WeplottheembeddingsforeachstudyintheACDCdatasetontotwodimen-sionsandconfirmthatcomparedtoabaselinenetworkofthesamearchitecture,ourvisionsystemisabletoseparatedifferentdiseaseconditionswithremarkableconsistency(SupplementaryFig.4).Thisisdespitethelackofanydirectedsupervisionduringthetrainingprocessinseparatingconditionssuchashypertrophiccardiomyopathyfromdilatedcardiomyopathyorrightventriculardysfunction.

ValidationontheUKBioBank

Wesourced159,883cine-CMRscansrepresenting45,623participantsfromtheUKBioBankandassessedtheperformanceoflarge-scalecontrastivepre-trainingonadatasetrepresentingarelativelyhealthyparticipantpopulationfromageographicallydistinctcontinent.Wehypothesizedthatifusefulrepresentationsarelearnedbyourdeep-learningsystemduringthepre-trainingphase,itshouldbetrivialtoseparateparticipantsonthebasisoffeaturesindicativeofdiseasewithoutanyadditionalsupervisedtraining.Weuseourcontrastivepre-trainednetworkandfreezetheweightstopreventanyadditionallearning,followingwhichwepasseachavailablecine-CMRsequencethroughthenetwork.Thenetworkgeneratesalow-dimensionalembeddingofeachinput.Weprocesseachviewseparatelyandstoretheembeddingsgeneratedforfurtherprocessing.AswiththeexperimentsontheACDCdataset,werepeattheexperimentwithanidenticalnetworkwithweightsinitializedfromatrainingrunontheKinetics-400actionrecognitiondataset,avideodatasetofnaturalsceneryandactivities

29

.

Weelectedtousetheunsupervisedt-distributedstochasticneighbourembedding(t-SNE)algorithmtodimensionallyreducethe

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aLarge-scalecontrastivepre-training

Videotransformer(36milliontrainableparams)

Theleftventricleexhibits

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Fig.1|Projectoverview.a,Large-scalecontrastivepre-training.CMRcine

sequencesintheformofvideodataarefedtoavideotransformernetwork,

andthepairedtextreportsarefedintoaparalleltexttransformernetwork.

Theembeddingsproducedbyeach(V,video;T,text)representcompressed

representationsofthecomplexinputstoeachnetwork.ThenetworksaretrainedtomaximizeagreementbetweentruepairsofvideoandtextthatarisefromthesameCMRscan(thatis,thediagonalofthesimilaritymatrixhighlighted:V1T1,

V2T2…VnTn)andminimizetheagreementbetweensamplesthatcomefromdifferentscans.b,Intheprocessina,weseethatastrainingprogresses,morecomplexlocalandglobalstructuralfeaturesemergeinthevideoembeddings(V).Thescansinitiallyviewedasanindistinctamalgaminhigh-dimensionalspaceatthestartoftraining(epoch#1)begintodevelopseparationsand

localizedclustersthataremoredistinctbyepoch#100.c,Evaluatingtheabilityofthenetwork,onexternaldatasets,inseparatingdiseasesorprobingtheUK

BioBankasdescribedinlatersectionsisachievedbyfreezingalltheparametersofthevideoencoder(atthispoint,thetextnetworkisdiscarded).Embeddingsproducedfromdifferentviewscanthenbeplottedin2Dviastandarddimensionreductionalgorithms.d,Finally,leavingthelastlayerunfrozen(~600,000

trainableparameters),weshowcasedata-efficientfinetuningtowardsspecificclinicaltasksofinterest.Ofnote,theembeddingsarefedintoasecondary

networkdesignedtoaggregateinformationfromdifferentviewsintoasingleprediction,withafewexampleslisted:regressionofleft-ventricularejectionfraction(LVEF%)anddiagnosisofamyloidosisorischaemiccardiomyopathy.Asinglevisionmodelcanthusbeappliedtoawiderangeofdifferenttasks.

embeddingsgeneratedforeachviewandvisualizethemintwodimen-sions

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.Weexpectedthenetworksatbaselinetoatleastbecapableofgeneratingembeddingsofsufficientqualitytoseparatedifferentviewswhenvisualizedwitht-SNE,astheyareobviouslydistincteventotheuntrainedeye.InFig.

2

(left),weshowtheunsupervisedclusteringofembeddingsproducedbyourcontrastivepre-trainednetworkin2Dt-SNEspace,forall45,623UKBioBankparticipants.Wefindthatthepre-trainednetworkisessentiallyviewinvariant.Keytodrivinginvari-ancebasedonviewplaneswastoensurethatallviewplanesfromthesamestudyarealignedwithatextembeddingfromthesametextreportduringpre-training.Thisallowsforattentiontobeplacedinsteadon

featuresofimportancehighlightedinthetext.Weseearelativelydenseclusterofpatientswithejectionfractions<35%.Surprisingly,withoutanyexplicitinstruction,wefindthatcontrastivepre-trainingallowedforsharpdelineationofsexandseparationbyage.Itislikelythatthepresenceoftextualinformationcontainingphrasessuchas‘adultfemale’withinsomeoftheMRIreportsallowedthevisionnetworkstolearnfeaturesthatidentifydemographiccharacteristics.Importantly,allofthesefindingsareseenwiththenetworksentirelyfrozen,withnoadditionallearningormodelparameterupdatespossible.ComparisonwithsimilarexperimentsusingtheKinetics-400pre-trainedbaselineisinstructiveindemonstratingthelackofanysuchbiomedicallatent

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Fig.2|Contrastivepre-trainingenableslearningofdemographicand

pathophysiologicalrepresentations.2Dt-SNEplotsoflow-dimensional

embeddingsgeneratedfromaforwardpasson34,490uniquescansfromtheUKBioBank(n=31,693uniqueindividuals)usingaKinetics-400pre-trainedcheckpoint(baseline,rightcolumn)comparedtoacontrastivepre-trained

checkpoint(ours,leftcolumn).Thecolourlabelshelpwithunderstanding

thebasisofclustering.WhileatfirstglanceitmayappearthattheKinetics

baselinemodelproducesasetof5readilyseparableclustersvsthemore

homogeneous-appearingplotforthecontrastivepre-trainedmodel,the

basisofthebaselineKinetics-initializedclustersaresimplythevariousMRI

viewplanes.OnfurtherexplorationwefindthattheKinetics-400-generatedembeddingsfailtocapturetherequiredinformationtoseparatelow-ejectionfractionstates,cardiomyopathy,genderorage(lackofseparationofcolourforeachvariable).Thisisdifferentfromthecontrastivepre-trainedgeneratedembeddingsthatshowcleardemarcationsbyphysiologicallyrelevant

characteristics,allowingforzero-shotseparationoflow-ejectionfraction

states,cardiomyopathy,genderandage.Colourlegendsforeachsubplotareshownontherightofeacht-SNEfigure.Viewinvarianceisabuilt-infeatureofthecontrastivepre-trainingprocess,evidencedbythecharacteristicabsenceofclusteringbyviewplane.

knowledgeviatraditionaltrainingmethods(Fig.

2

right).NodistinctclustersbeyondthoseofthemajorMRIviewplanesstudiedareseenwithoutourcontrastivepre-trainingmethods.

Self-attention-basedaggregationofembeddingsfrommultipleCMRviews

Weperformaseriesofexperimentstodefinetheimpactofcontrastivepre-trainingwhenthesemodelsaretaskedtoavarietyofclinicallyrel-evantregressionandclassificationproblems.EachviewinacardiacMRIstudymaybeofvaryingdiagnosticutility,dependingonthepresent-ingpathology.TomimictheapproachofcliniciansreportingcardiacMRIscans,asecondaryneuralnetworkisusedtoaggregateusefulinformationfromeachviewplanetogenerateafinalscanleveloutput(Methods).Asaresult,ourmodelsuseallavailableviews(4CH,2CH,3CHandSAX)whileanalysingeachpatientexamination.WetreatthepresenceofmultipleMRIviewplanesasaweaklylabelledmulti-instancelearningproblem.Videosfromeachviewplaneareprocessedviaourpre-trainedvideotransformerencoder,andtheresultingembeddingsarefedintoaself-attention-basedmulti-instancelearningmodule.Theself-attentionmoduleistrainedtoidentifyrelevantfeaturesacrosseachavailableviewplane,independentoftheactualnumberofviewssup-plied

31

.Therelativecontributionofoneviewoveranothertowardsthefinaloutputiscalculatedviaalearnedweightedaverageoftheoutputembeddings.Thisassignsimportancetodifferentviewsdependingonthepathologicalfeaturespresentinthem.Thisisconceptuallyidenticaltoassigninghigherimportancetoaselectionofzoomed-inpatchesofhistopathologywhole-slideimagesthatcontainfeaturesofmalignancy,inthebackgroundofalargenumberofnon-diagnosticpatches

13

,

31

.

Automatedestimationofleft-ventricularejectionfraction

Wedefinetheimpactofcontrastivepre-trainingontheproblemofpredictingleft-ventricularejectionfraction(LVEF),acommonlycalculatedmetricofcardiacfunction,ontwoexternaldatasets:theUKBioBankreferencedaboveandapubliclyavailableCMRdataset(Kaggle)forthisproblem.Deep-learning-derivedautomatedLVEFmeasurementsinechocardiographyandcardiacMRIaretypicallybasedonsegmentationmodelsorhybridregression-segmentationsystems

4

,

28

,

32

,

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.Suchnetworksaretrainedtooutlineleftventricularchambersduringendsystoleandenddiastole,andassuchhavenofurtherinherentunderstandingofthediseasespace.Nonetheless,thesenetworksperformexceptionallywellinthetaskofLVEFestimationastheyreplicatethemeasurementworkflowofroutineclinicalpractice.Forinstance,ameanabsoluteerror(MAE)of3.2hasbeenreportedforthetaskofpredictingLVEFinasubsetoftheUKBioBankparticipantsusingshort-axissequences

34

.Tworecentpapersreleasedwhileourmanuscriptwasinreviewarenotablefortheirtechnicaladvanceintheareaofdeep-learning-poweredLVEFestimation:ref.

35

describesCineMA,amasked-autoencodersystemutilizingahybrid-segmentationsupervision;andref.

36

describesmaskedautoencoderscombinedwithacontrastiveobjective.WhilebothachievecompetitiveperformanceonLVEFestimationintheUKBioBank(CineMAwithaMAEof3.34%andref.

36

withaMAEof2.95%),critically,thesesystemswerebothpre-trainedandfinetunedontheUKBioBankitself,makingdirectcomparisonsofgeneralizabilitychallenging.

Prospectivestudieshaveshownthatthereisconsiderablevari-abilityininstitutionalanddataset-specificprotocolsforcalculationofcertainmetrics,butclinicianscantypicallybeexpectedtomakeestimatesofLVEFwithinBland–Altmanlimitsof_12%to+12%

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,

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.Withourpre-trainedvisionencoderfrozen,barringthelastlinearlayer,wefinetunethenetworkon34,488uniqueCMRscansfromtheUKBioBankforthetaskofpredictingLVEF.Thevisionencodercannolongerlearndataset-specificfeatureswiththisapproachandmustrelyonlearnedrepresentationalabilitiesfrompreviouspre-training.Toincorporateinformationfromeachavailableviewtoproducea

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‘patientlevel’predictionofLVEF,weuseamulti-instanceself-attentionregressionheadasdescribedaboveandinputcine-CMRsequencesfromallavailableviewswithoutanyadditionalqualitycontrolsteps(Methods).Onahold-outtestsubsetofUKBioBankparticipantsusingourapproach,wereportaMAEof3.344(s.d.3.615),withBland–Altmanlimitsofagreementof_9.91%to+9.61%.Atbaseline,usingatradition-allytraineddeep-learningsystemofthesamemodelarchitectureasourcontrastivepre-trainedmodels,wereportanMAEof4.603(s.d.4.409),withBland–Altmanlimitsofagreementof_12.15%to+12.8%.

Thesemetricsrivalthoseofhand-crafteddeep-learningsystemstocalculateLVEFusingsegmentationmasksonmanuallyselectedend-systolicandend-diastolicframes,andarewellwithinerrorlimitsofcliniciansfollowingstrictannotationprotocols

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,

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.Wefreezethisfine-tunedmodelandevaluateitoncardiacMRIscansfromapub-liclyavailabledataset,wherepatientswererecruitedfromhospitalsystemsbasedintheUnitedStates(KaggleDataScienceBowl)

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.TheKaggleexternaldatasetcontainsalargerproportionofpatientswithdiseasedheartsandadditionallyutilizesaslightlydifferentscanningprotocolandmethodfordeterminationofground-truthLVEFlabels.OnthisdatasetwereportaMAEof6.880(s.d.5.309),withBland–Alt-manlimitsofagreementfo_18.7%to+8.03%.GiventhedifferencesinannotationmethodscomparedtodatafromtheUKBioBank,thefrozenfine-tunednetworkshowedamodestsystematicunderpredictionofLVEFby5.36%(95%CI4.86–5.87)(Fig.

3b

).Diagnosticplotsfortest-setresultsandadditionalresultswithimprovementsontheKaggledatasetwithbiascorrectionareavailableinSupplementaryFigs.7and8(finalbias-correctedMAE4.861),showingsuperiorperformanceinthehighandlowLVEFrange,withsmallerresidualerrorswhenmodelsareinitial-izedfromcontrastivepre-trainedweights.Finally,wemanuallyreviewCMRstudiesformodelpredictionswiththelargestabsoluteerrorsandfindthatthemajorityofthesestemfromincorrectground-truthlabelsorartefact/noise-degradedimages(SupplementaryFig.9andTable4).

Nevertheless,ifthesepredictedLVEFvaluesweretobeusedforidentifyingpatientswithheartfailurewithreducedejectionfraction(HFrEF)<40