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• W4205807785 abstract "Hydronephrosis may lead to many potential diseases, and the diagnosis of hydronephrosis is time-consuming and laborious. To assist physicians in hydronephrosis diagnosis and treatment planning, an accurate and automatic kidney segmentation method is highly required in clinical practice. In recent years, deep convolutional neural networks such as Unet plays a key role in the field of image segmentation, but Unet itself cannot adjust the receptive field actively, which may result in poor attention to the characteristics of the segmented target. We propose an encoder-decoder network with weighted skip connections and the idea of hierarchical equal resolution that can manually control the receptive field. We evaluated our method by comparing it with various classical networks using a dataset of 1850 annotated images. The MPA of the model is 94.12 and the MIoU is 89.49, which outperformed other classical networks we compared to." @default.
• W4205807785 created "2022-01-26" @default.
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• W4205807785 date "2021-10-17" @default.
• W4205807785 modified "2023-10-16" @default.
• W4205807785 title "MwUnet: A semantic segmentation deep learning method for the ultrasonic image of hydronephrosis in children" @default.
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• W4205807785 doi "https://doi.org/10.1109/smc52423.2021.9658930" @default.
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