![]() Many thanks to my supervisor Michael Zerres for his guidance and our conversation. The author would like to thank the following for their co operation in helping with this report: Culture values according and regional differences in Chinaģ.5 Differences among regional markets in Chinaģ.5.1 The seven regional markets of Chinaģ.5.2 Geographic diversity and economic disparityģ.5.3 Dramatic differences among customersģ.5.5 Significant differences in consumption patternsģ.5.6 Income has had a direct impact on demandĥ.2.1 The functions of a brand for consumersĥ.2.2 The functions of brands from a company’s perspectiveĥ.3 A brand building process based on core valuesĥ.5 The Keller brand equity approach and the model of the brand- natureĥ.7 Suitable brand-drivers for the Chinese marketĥ.7.1 Brand name and Country-of-origin effectĥ.9 Basic economics legal conditions for foreign investorsĥ.9.3.1 Contract law of the People’s Republic and legal choice for contract negotiationsĥ.9.3.2 Expiry of the contract negotiationsĦ.1 Brand naming and brand name translationĦ.1.1 The need for brand name translation into ChineseĦ.1.2 Brand Naming in International MarketingĦ.1.3 Critical methods for brand naming in ChinaĦ.1.4 Critical aspects for brand naming in ChinaĦ.2.1 Logo design of foreign brands in ChinaĦ.2.2 Logo design of foreign brands according to aesthesticsĦ.3.2 Packing elements according to Chinese luxury goodsħ.2 Relationship between brand and target groupħ.2.3 Relationship between brand and consumerħ.5 Relationship communication in China by advertisementsħ.6.1 Informational versus Emotional Advertisementsħ.6.2 Essential notions and important backgroundsħ.7.2 Personal and interactive communication toolsħ.8 Suggestions for brand- advertisements Performance of chest radiograph interpretation models.3. Release the dataset to the public as a standard benchmark to evaluate Model ROC and PR curves lie above all 3 radiologist operating points. On Cardiomegaly, Edema, and Pleural Effusion, the Performance of our model to that of 3 additional radiologists in the detection Our best model on a test set composed of 500 chest radiographic studiesĪnnotated by a consensus of 5 board-certified radiologists, and compare the Uncertainty approaches are useful for different pathologies. Manually annotated by 3 board-certified radiologists, we find that different On a validation set of 200 chest radiographic studies which were Probability of these observations given the available frontal and lateral Uncertainty labels for training convolutional neural networks that output the We investigate different approaches to using the Observations in radiology reports, capturing uncertainties inherent in We design a labeler to automatically detect the presence of 14 We presentĬheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 ![]() Download a PDF of the paper titled CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison, by Jeremy Irvin and 19 other authors Download PDF Abstract: Large, labeled datasets have driven deep learning methods to achieveĮxpert-level performance on a variety of medical imaging tasks.
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