Osteosarcoma is one of the most common bone tumors that occurs in adolescents. Doctors often use magnetic resonance imaging (MRI) through biosensors to diagnose and predict osteosarcoma. However, a number of osteosarcoma MRI images have the problem of the tumor shape boundary being vague, complex, or irregular, which causes doctors to encounter difficulties in diagnosis and also makes some deep learning methods lose segmentation details as well as fail to locate the region of the osteosarcoma. In this article, we propose a novel boundary-aware grid contextual attention net (BA-GCA Net) to solve the problem of insufficient accuracy in osteosarcoma MRI image segmentation. First, a novel grid contextual attention (GCA) is designed to better capture the texture details of the tumor area. Then the statistical texture learning block (STLB) and the spatial transformer block (STB) are integrated into the network to improve its ability to extract statistical texture features and locate tumor areas. Over 80,000 MRI images of osteosarcoma from the Second Xiangya Hospital are adopted as a dataset for training, testing, and ablation studies. Results show that our proposed method achieves higher segmentation accuracy than existing methods with only a slight increase in the number of parameters and computational complexity.
Social networks are very important information dissemination platforms. Messages in social networks are forwarded through encounters formed by moving nodes in opportunistic complexity network. However, with the gradual expansion of the network scale, the amount of data to be transmitted has increased dramatically, which has brought greater load to the entire network. In order to improve this transmission environment, this paper proposes a transmission mechanism that selects cooperative nodes based on the user's interest. In communication, the degree of matching of node preferences is calculated, and highly matched nodes are selected for priority transmission. This method reduces the occupation of cache resources by redundant information in the network by filtering the message transmission mode of the cooperative nodes, and improves the space utilization of the message cache. Through the comparison of experiments and social network algorithms, this method improves the node's cache utilization, reduces the data transmission delay, and improves the overall network efficiency.
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