Titlebook: Internal Migration and Health in China; Choices, Constraints Yan Li Book 2024 The Editor(s) (if applicable) and The Author(s), under exclus

單獨(dú)

Yan Lis. However, the expensive exercise of obtaining pixel-level annotations for large amounts of data is not always feasible, and performance is often heavily reduced in a low-annotated data regime. To tackle this challenge, we adapt a mixed supervision framework, vMFNet, to learn robust compositional r

Jacket

Yan Liitectures. Such approaches, however, typically ignore domain specific peculiarities and lack the ability to generalize outside their training dataset. We observe that, in RGB images, teeth display a weak or unremarkable texture while exhibiting strong boundaries; similarly, in panoramic radiographs

Munificent

growth of cells according to cytotoxicity grade under the microscope. Thus, human fatigue plays a role in error making, making the use of deep learning appealing. Due to the high cost of training data annotation, an approach without manual annotation is needed. We propose ., a new method for trainin

策略

ARC

implore

Constraints on Health and Health Services Access: One Class of Migrants?,raints on health and health service access in respect of the social strata of rural–urban migrants. After the discussion of three strata among migrants, the chapter analyses the financial reasons for the hierarchical health access and pays particular attention to the specific constraints on health a

卵石

tenuous

孵卵器

Discussion and Conclusion,ns for access to basic urban public services, social security and the solidification of migrants’ identity, and implications for integrating the social insurance systems for urban and rural residents and urban employees are particularly discussed in this chapter. The importance of this study and the

慢跑

https://doi.org/10.1007/978-3-663-19785-0hese types of approaches at the international, regional, and national levels while considering outcomes achieved. Finally the chapter will conclude by critically examining challenges in implementing these approaches for effective surveillance, monitoring, and evaluation.