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Early computer-aided early diagnosis (CAD) based on retinal imaging is critical to the timely management and treatment planning of retina-related diseases. However, the inherent characteristics of retinal images and the complexity of their pathological patterns, such as low image contrast and different lesion sizes, restrict the performance of CAD systems. Recently, ultra-wide-field (UWF) retinal images have become a useful tool for disease detection due to the capability of capturing much broader view of retina (i.e., up to 200°), in comparison with the most commonly used retinal fundus images (45°). In this paper, we propose an attention-based multi-branch network for the diseases classification of four different subject groups. The proposed method consists of a multi-scale feature fusion module and a dual attention module. Specifically, small-scale lesions are identified using the features extracted from the multi-scale feature fusion module. To better explore the obtained features, the dual attention module with a global attention graph is incorporated to enable the network to recognize the salient objects of interest. Comprehensive validations on both private and public datasets were carried out to verify the effectiveness of the proposed model.

Cities are centers for the integration of capital and incubators of inventions. Attracting venture capital (VC) is of great importance for cities to advance in innovative technology and business models towards a sustainable and prosperous future. Yet we still lack a quantitative understanding of the relationship between urban characteristics and VC activities. In this paper, we find a clear nonlinear scaling relationship between VC activities and the urban population of Chinese cities. In such nonlinear systems, the widely applied linear per capita indicators would be either biased to larger cities or smaller cities depends on whether it is superlinear or sublinear, while the residual of cities relative to the prediction of scaling law is a more objective and scale-invariant metric. Such a metric can distinguish the effects of local dynamics and scaled growth induced by the change of population size. The spatiotemporal evolution of such metrics on VC activities reveals three distinct groups of cities, two of which stand out with increasing and decreasing trends, respectively. The taxonomy results together with spatial analysis also signify different development modes between large urban agglomeration regions. Besides, we notice the evolution of scaling exponents on VC activities are of much larger fluctuations than on socioeconomic output of cities, and a conceptual model that focuses on the growth dynamics of different sized cities can well explain it, which we assume would be general to other scenarios.