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• W4380052967 abstract "Abstract Medical image segmentation is crucial for the diagnosis and analysis of disease. Deep convolutional neural network methods have achieved great success in medical image segmentation. However, they are highly susceptible to noise interference during the propagation of the network, where weak noise can dramatically alter the network output. As the network deepens, it can face problems such as gradient explosion and vanishing. To improve the robustness and segmentation performance of the network, we propose a wavelet residual attention network (WRANet) for medical image segmentation. We replace the standard downsampling modules (e.g., maximum pooling and average pooling) in CNNs with discrete wavelet transform, decompose the features into low- and high-frequency components, and remove the high-frequency components to eliminate noise. At the same time, the problem of feature loss can be effectively addressed by introducing an attention mechanism. The combined experimental results show that our method can effectively perform aneurysm segmentation, achieving a Dice score of 78.99%, an IoU score of 68.96%, a precision of 85.21%, and a sensitivity score of 80.98%. In polyp segmentation, a Dice score of 88.89%, an IoU score of 81.74%, a precision rate of 91.32%, and a sensitivity score of 91.07% were achieved. Furthermore, our comparison with state-of-the-art techniques demonstrates the competitiveness of the WRANet network." @default.
• W4380052967 created "2023-06-10" @default.
• W4380052967 creator A5020070155 @default.
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• W4380052967 date "2023-06-08" @default.
• W4380052967 modified "2023-09-30" @default.
• W4380052967 title "WRANet: wavelet integrated residual attention U-Net network for medical image segmentation" @default.
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• W4380052967 doi "https://doi.org/10.1007/s40747-023-01119-y" @default.
• W4380052967 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37361970" @default.
• W4380052967 hasPublicationYear "2023" @default.
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