Cross-Reference Transformer for Few-shot Medical Image Segmentation

Due to the contradiction of medical image processing, that is, the application of medical images is more and more widely and the limitation of medical images is difficult to label, few-shot learning technology has begun to receive more attention in the field of medical image processing. This paper proposes a Cross-Reference Transformer for medical image segmentation, which addresses the lack of interaction between the existing Cross-Reference support image and the query image. It can better mine and enhance the similar parts of support features and query features in high-dimensional channels. Experimental results show that the proposed model achieves good results on both CT dataset and MRI dataset.Comment: 6 pages,4 figure

DIGITAL TRANSFORMATION AND ORGANIZATIONAL DYSFUNCTIONS: A CASE STUDY IN OPERATION IN CHINA

The socio-technical perspective has been recognized as the mainstream of Information Systems (IS) philosophy for decades. Besides, a complementary perspective in the IS philosophy, the socio-economic theory, would allow identifying more precisely the business problems, considered “Organizational Dysfunction”. Digital transformation is supposed to fix the specific business problem as cross-department communication, and it is essential to involve the analysis of organizational dysfunctions ahead. A case study was conducted in operation in China, where digital transformation was implemented to solve cross-department communication business problems. Beyond this specific business problem, this case study relies on the relevance of the theories “Organizational Dysfunctions” and “Socio-Economic Approach to Management (SEAM).” Focus group was adopted to figure out the key business problem, and semi-structured interviewing for the main root causes. It revealed digital transformation significance on the inefficient cross-department communication through the identification of the analytical results and the theories of Organizational Dysfunctions and SEAM

Robust passivity of coupled Cohen-Grossberg neural networks with reaction-diffusion terms

In this paper, we deal with the robust passivity problem for coupled reaction-diffusion Cohen-Grossberg neural networks (CRDCGNNs) with spatial diffusion coupling and state coupling. First, we present the network model for CRDCGNNs with state coupling and establish some robust passivity conditions for this kind of CRDCGNNs. Then, the investigation on robust passivity for CRDCGNNs with spatial diffusion coupling is carried out similarly. At last, the feasibility of the obtained theoretical results is demonstrated by one example with simulation results