SP-CPGCN：用于塵肺病分期的超像素先驗因果感知圖卷積網(wǎng)絡

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SP-CPGCN ： causality-perception graph convolutional network on superpixel prior for pneumoconiosis staging

Wang Yueyingla，Ji Guohuala，F(xiàn)eng Weiyi1a，Zhao Juanjuanla，Ibt，Qiang Yanla.2，MaJianfenla，Shi Yiwei 3 ，Yang Fan4 （1.a.Collegfeeceeece）odfrenUiesit 030024，Chna；2holfSfare，NUniersitfhnaTaianO3OhnaDeptofulmary&ricalCaredicine ShanxiKeybsoeosliclUsi 030012，China;4Ocupioalisesereetio&ontroltefisnCal&Electricity（Gou）anna）

Abstract：Toaddress thelowaccuracyinpneumoconiosisstaging causedbythesmallandthinpneumoconiosis fociandthe influenceof non-causal features inexistingdeeplearning methods，thispaperproposedacausality-perceptiongraph convolutionalnetworkonsuperpixelpriors（SP-CPGCN）.Themethodextractedlocal，subtlefeaturesbyperforming featureextractiononsuperpixelsratherthantheentirechestradiograph.The method constructedagraph network byfullyconsidering the spatial proximityandfeaturesimilarityamongnodes，anddesignedahierarchicalaggegatedgraphconvolutionalnetwork to enableinformationtransferandfeatureintegrationacrossdiffrentdepths.Aditionally，themethodemployedadaptivecausal inferenceongraphconvolutionalnetworks，usingacausalinterventionstrategythatcombinedinterventionlossandstabilityloss to avoidheinterfereneof non-causalfeatures.Itlsointroducedanintraclassconsistencylosstoalance individual-specific features with group-universal features.Thevalidationresultsonaclinicalpneumoconiosischestradiograph datasetshowthat SP-CPGCN achieved an accuracy of 82.4% ，a precision of 78.9% ，a sensitivity of 77.3% ，a specificity of 88.6% ，and an AUC of 90.3% ，outperforming other methods. The experimental results show that SP-CPGCN efectively improves the accuracyand stability of pneumoconiosis staging and provides areliablenew method for automated medical diagnosis.

'ey words：graph convolutional network（GNN）；causal inference； superpixel; chest X-ray；pneumoconiosis stagi

0 引言

塵肺病是一種不可逆轉(zhuǎn)的呼吸系統(tǒng)疾病。（剩余19069字）