Aiming at the problems in the existing methods such as dependence on prior knowledge， insufficient robustness， and insufficient expression ability in table structure recognition， a new table structure recognition model integrating edge features and attention was proposed， namely Graph Edge-Attention Network based Table Structure Recognition model （GEAN-TSR）. Firstly， Graph Edge-Attention Network （GEAN） was proposed as the backbone network， and based on edge convolution structure， the graph attention mechanism was introduced and improved to aggregate graph node features， so as to solve the problem of information loss in the process of feature extraction of graph network， and improve the expression ability of graph network. Then， an edge feature fusion module was introduced to fuse the shallow graph node information with the graph network output to enhance the local information extraction and expression abilities of the graph network. Finally， the graph node text features extracted by Gated Recurrent Unit （GRU） were integrated into the text feature fusion module for edge’s classification and prediction. Comparative experiments on Scientific paper Table Structure Recognition-COMPlicated （SciTSR-COMP） dataset show that the recall and F1 score of GEAN-TSR are increased by 2.5 and 1.4 percentage points， respectively in comparison with the existing optimal model Split， Embed and Merge （SEM）. Ablation experiments show that all the indicators of GEAN-TSR have achieved the optimal values after using the feature fusion module， proving the effectiveness of the module. Experimental results show that GEAN-TSR can effectively improve the network performance and better complete the task of table structure recognition.

In recent years， Generative Adversarial Network （GAN） has become a new research hotspot in Low-Dose Computed Tomography （LDCT） artifact suppression because of its performance advantages. Due to irregular distribution and strong relevance to the normal tissues of artifacts， denoising performance of the existing GAN-based denoising networks is limited. Aiming at this problem， a LDCT denoising algorithm based on artifacts sensing GAN was proposed. Firstly， an artifacts direction sensing generator was designed. In this generator， on the basis of U-residual encoding and decoding structure， an Artifacts Direction Sensing Sub-module （ADSS） was added to improve the generator’s sensitivity to artifacts direction features. Secondly， the Attention Discriminator （AttD） was designed to improve the ability of distinguishing noise and artifacts. Finally， the loss functions corresponding to the network functions were designed. Through the cooperation of multiple loss functions， the denoising performance of network was improved. Experimental results show that compared to the High-Frequency Sensitive GAN （HFSGAN）， the proposed denoising algorithm has the average Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） improved by 4.9% and 2.8% respectively， and has good artifact suppression effect.