BACKGROUND AND OBJECTIVE: Since low-dose computed tomography (LDCT) images typically have higher noise that may affect accuracy of disease diagnosis, the objective of this study is to develop and evaluate a new artifact-assisted feature fusion attention (AAFFA) network to extract and reduce image artifact and noise in LDCT images. METHODS: In AAFFA network, a feature fusion attention block is constructed for local multi-scale artifact feature extraction and progressive fusion from coarse to fine. A multi-level fusion architecture based on skip connection and attention modules is also introduced for artifact feature extraction. Specifically, long-range skip connections are used to enhance and fuse artifact features with different depth levels. Then, the fused shallower features enter channel attention for better extraction of artifact features, and the fused deeper features are sent into pixel attention for focusing on the artifact pixel information. Last, an artifact channel is designed to provide rich artifact features and guide the extraction of noise and artifact features. The AAPM LDCT Challenge dataset is used to train and test the network. The performance is evaluated using both visual observation and quantitative metrics including peak signal-noise-ratio (PSNR), structural similarity index (SSIM) and visual information fidelity (VIF). RESULTS: Using AAFFA network improves the averaged PSNR/SSIM/VIF values of AAPM LDCT images from 43.4961, 0.9595, 0.3926 to 48.2513, 0.9859, 0.4589, respectively. CONCLUSIONS: The proposed AAFFA network enables to effectively reduce noise and artifacts while preserving object edges. Assessment of visual quality and quantitative index demonstrates the progressive improvement compared with other image denoising methods.
Abstract. green-credit policy is one of the important channels for the conversation and development of environmental protection, it is also the main direction for Chinese commercial bank to implement reform and development, which arises high attention from all sectors of community. This paper is based on evolutionary game theory to discuss the factors of affecting green-credit policy implemented by commercial bank. It uses DID model to implement demonstration data analysis, learn its effect on performance of commercial bank. Results indicate that increasing green-credit policy revenue, reducing cost, strengthening the punishment cost without implementing green-credit policy can effectively promote the initiative of commercial bank to implement green-credit policy. Although the current effect is not so obvious, green-credit policy of commercial bank will bring plenty of advantages with the policy improvement and time progress, we should continually implement green-credit policy.
Abstract.Based on the developing features of the energy finance in our country, this paper designs the warning index system covering the systematic features of energy finance risks, which is BP neural network method based on genetic algorithm optimization; the energy finance risk warning model can provide information on risk recognition and risk warning for government and relevant management departments.
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