MEF-UNet: An end-to-end ultrasound image segmentation algorithm based on multi-scale feature extraction and fusion.

Ultrasound image segmentation is a challenging task due to the complexity of lesion types, fuzzy boundaries, and low-contrast images along with the presence of noises and artifacts. To address these issues, we propose an end-to-end multi-scale feature extraction and fusion network (MEF-UNet) for the automatic segmentation of ultrasound images. Specifically, we first design a selective feature extraction encoder, including detail extraction stage and structure extraction stage, to precisely capture the edge details and overall shape features of the lesions. In order to enhance the representation capacity of contextual information, we develop a context information storage module in the skip-connection section, responsible for integrating information from adjacent two-layer feature maps. In addition, we design a multi-scale feature fusion module in the decoder section to merge feature maps with different scales. Experimental results indicate that our MEF-UNet can significantly improve the segmentation results in both quantitative analysis and visual effects.

U structured network with three encoding paths for breast tumor segmentation.

Breast ultrasound segmentation remains challenging because of the blurred boundaries, irregular shapes, and the presence of shadowing and speckle noise. The majority of approaches stack convolutional layers to extract advanced semantic information, which makes it difficult to handle multiscale issues. To address those issues, we propose a three-path U-structure network (TPUNet) that consists of a three-path encoder and an attention-based feature fusion block (AFF Block). Specifically, instead of simply stacking convolutional layers, we design a three-path encoder to capture multiscale features through three independent encoding paths. Additionally, we design an attention-based feature fusion block to weight and fuse feature maps in spatial and channel dimensions. The AFF Block encourages different paths to compete with each other in order to synthesize more salient feature maps. We also investigate a hybrid loss function for reducing false negative regions and refining the boundary segmentation, as well as the deep supervision to guide different paths to capture the effective features under the corresponding receptive field sizes. According to experimental findings, our proposed TPUNet achieves more excellent results in terms of quantitative analysis and visual quality than other rival approaches.

Can the social trust promote corporate green innovation? Evidence from China.

Corporate green innovation has played a crucial role in balancing profitability and environmental protection. The existing research on determinant factors of green innovation has its main defects in emphasizing excessively enterprise's formal institutional environment and neglecting the informal institutional environment, causing an incomplete understanding of the relationship between institutional environments and corporate green innovation. To bridge this gap, using a sample of Shanghai and Shenzhen A-share listed firms in manufacturing industry during the period of 2010-2016, we investigate how social trust, an important informal institutions, affects corporate green innovation. Our results show that social trust is positively associated with green innovation, remaining valid after applying endogenous and robustness tests. In addition, the positive relationship between social trust and green innovation is more prominent when the enterprise is non-state-owned or locates in a looser command-and-control (CAC) environmental regulations region. Further analysis shows that social trust boosts corporate green innovation via promoting knowledge sharing, decreasing financing constraints, and fulfilling more corporate social responsibility (CSR). This paper enriches the literature concerning social trust and green innovation and draws back more public attention on the role of informal institutions play in promoting green innovation.