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一種基于深度學習的二階段舌象分割網(wǎng)絡模型AbstractTonguesegmentationplaysanimportantroleindiagnosingoraldiseasesandanalyzingtraditionalChinesemedicinetonguediagnosis.However,traditionalsegmentationmethodsoftenrelyonhandcraftedfeatures,whichhavelimitedperformanceincomplexscenes.Therefore,weproposeatwo-stagetonguesegmentationmethodbasedondeeplearning.Inthefirststage,weemploytheU-Nettoobtaintheinitialsegmentationresult.Then,weusetheself-attentionmechanismtorefinethesegmentationresultinthesecondstage.Experimentalresultsshowthatourproposedmethodoutperformsstate-of-the-artmethodsintonguesegmentationonthedatasetwecollectedandontwopublicdatasets.IntroductionInrecentyears,tonguediagnosishasbecomeanincreasinglypopularandimportantmethodintraditionalChinesemedicine.Tonguesegmentation,asacriticalstepoftongueanalysis,aimstoaccuratelyextracttheregionsofthetonguefromtheimage,whichcaneffectivelyaidindiagnosingdiseasesandanalyzingtonguefeatures.However,traditionalsegmentationmethodsoftenrelyonhandcraftedfeatures,whichhavelimitedperformanceincomplexscenes.Overthepastfewyears,deeplearning-basedmethodshavedemonstratedremarkableperformanceindifferentsegmentationtasks,whichmotivatesustodesignadeeplearning-basedmethodfortonguesegmentation.Inthispaper,weproposeatwo-stagetonguesegmentationmethodbasedondeeplearning,whichconsistsofaninitialsegmentationstageandarefinementstage.Intheinitialsegmentationstage,weemploytheU-Nettoobtaintheinitialsegmentationresult,whichisusedastheinputoftherefinementstage.Intherefinementstage,weusetheself-attentionmechanismtorefinetheinitialsegmentationresult,whichcaneffectivelycapturethedependenciesbetweenfeaturesandextractthesignificantregionsfortonguesegmentation.MethodologyOverallFrameworkTheproposedmethodconsistsoftwostages:theinitialsegmentationstageandtherefinementstage.TheoverallframeworkofourproposedmethodisshowninFigure1.Intheinitialsegmentationstage,ourproposedmethodemploystheU-Nettoobtaintheinitialsegmentationresult.Specifically,wefirstresizetheinputimageto256×256andtheninputitintoU-Nettoobtaintheinitialsegmentationresult.Intherefinementstage,weusetheself-attentionmechanismtorefinetheinitialsegmentationresult.Theself-attentionmechanismisintroducedtolearnthedependenciesbetweenthefeaturesandeffectivelycapturethesignificantregionsfortonguesegmentation.Bycombiningtheinitialsegmentationresultandtheattentionmapobtainedthroughtheself-attentionmechanism,weobtainthefinalsegmentationresult.ArchitectureofU-NetTheU-Netisapopularandefficientarchitectureforimagesegmentation,whichusesanencoder-decoderstructuretotransformtheinputimageintoacompactfeaturemapandthendecodeitintoasegmentationmap.ThearchitectureoftheU-NetisshowninFigure2.TheU-Netarchitectureismainlycomposedofanencoderandadecoder.Theencoderextractsthefeaturesoftheimagethroughtheconvolutionallayersanddownsamplingoperations,whilethedecoderusesupsamplingoperationsandtransposedconvolutionallayerstorecoverthespatialresolutionofthefeaturemaps.ArchitectureofSelf-AttentionMechanismTheself-attentionmechanismhasdemonstratedremarkableperformanceindifferenttasksforcapturinglong-rangedependencies.Thearchitectureoftheself-attentionmechanismisshowninFigure3.Theself-attentionmechanismismainlycomposedofthreelayers:thequerylayer,thekeylayer,andthevaluelayer.Thequerylayerobtainsthequerymatrix,whichisusedtomeasurethecorrelationbetweenthefeatures.Thekeylayerobtainsthekeymatrix,whichreflectsthepotentialimportanceofthefeatures.Thevaluelayerobtainsthevaluematrix,whichrecordsthefeaturesthatneedtobeattendedto.Thedotproductofthequerymatrixandthekeymatrixiscalculatedtoobtaintheattentionmap,whichisthenmultipliedbythevaluematrixtoobtaintheoutput.ExperimentalResultsDatasetsToevaluatetheperformanceofourproposedmethod,wecollectedanewdatasetconsistingof200tongueimageswithannotations.Inaddition,wealsotestedourproposedmethodontwopublicdatasets:theSTHdatasetandtheISIC2018dataset.EvaluationMetricsWeusedtheDicecoefficientandtheIntersectionoverUnion(IoU)astheevaluationmetricsfortonguesegmentation.QuantitativeResultsThequantitativeresultsofourproposedmethodandthecomparisonmethodsonthethreedatasetsareshowninTable1.OurproposedmethodachievesthehighestDicecoefficientandIoUscoresonallthreedatasets,whichdemonstratesthesuperiorityofourproposedmethodintonguesegmentation.QualitativeResultsThequalitativeresultsofourproposedmethodandthecomparisonmethodsonthetestsamplesofthethreedatasetsareshowninFigure4.Itcanbeobservedthatourproposedmethodeffectivelysegmentsthetongueregionwithclearboundariesandaccurateregions,whilethecomparisonmethodshavesomeerrorsinthesegmentation.ConclusionInthispaper,weproposeatwo-stagetonguesegmentationmethodbasedondeeplearning,whichconsistsofaninitialsegm

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