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  • Open Access


    Advancing Brain Tumor Analysis through Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis

    S. Kannan1,*, S. Anusuya2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3835-3851, 2023, DOI:10.32604/cmc.2023.042465

    Abstract Gliomas, the most prevalent primary brain tumors, require accurate segmentation for diagnosis and risk assessment. In this paper, we develop a novel deep learning-based method, the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis (DHA-ISSP) model. The DHA-ISSP model combines a three-band 3D convolutional neural network (CNN) U-Net architecture with dynamic hierarchical attention mechanisms, enabling precise tumor segmentation and survival prediction. The DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels, enhancing segmentation accuracy. By achieving remarkable results, our approach surpasses 369 competing teams in the 2020 Multimodal… More >

  • Open Access


    HSPM: A Better Model to Effectively Preventing Open-Source Projects from Dying

    Zhifang Liao1, Fangying Fu1, Yiqi Zhao1, Sui Tan2,3,*, Zhiwu Yu2,3, Yan Zhang4

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 431-452, 2023, DOI:10.32604/csse.2023.038087

    Abstract With the rapid development of Open-Source (OS), more and more software projects are maintained and developed in the form of OS. These Open-Source projects depend on and influence each other, gradually forming a huge OS project network, namely an Open-Source Software ECOsystem (OSSECO). Unfortunately, not all OS projects in the open-source ecosystem can be healthy and stable in the long term, and more projects will go from active to inactive and gradually die. In a tightly connected ecosystem, the death of one project can potentially cause the collapse of the entire ecosystem network. How can… More >

  • Open Access


    SPP1 and the risk score model to improve the survival prediction of patients with hepatocellular carcinoma based on multiple algorithms and back propagation neural networks


    BIOCELL, Vol.47, No.3, pp. 581-592, 2023, DOI:10.32604/biocell.2023.025957

    Abstract Hepatocellular carcinoma (HCC) is associated with poor prognosis and fluctuations in immune status. Although studies have found that secreted phosphoprotein 1 (SPP1) is involved in HCC progression, its independent prognostic value and immune-mediated role remain unclear. Using The Cancer Genome Atlas and Gene Expression Omnibus data, we found that low expression of SPP1 is significantly associated with improved survival of HCC patients and that SPP1 expression is correlated with clinical characteristics. Univariate and multivariate Cox regression confirmed that SPP1 is an independent prognostic factor of HCC. Subsequently, we found that T cell CD4 memory-activated monocytes,… More >

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