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• W4385655252 abstract "Medical image segmentation can effectively identify lesions in medicine, but some small and rare lesions cannot be well identified. Existing studies do not take into account the uncertainty of the occurrence of diseased tissue, and the problem of long-tailed distribution of medical data. Meanwhile, the grayscale image obtained from Magnetic Resonance Imaging (MRI) detection has problems, such as the features being difficult to extract and invalid features being difficult to distinguish. In order to solve these problems, we propose a new weighted attention ResUNet (WA-ResUNet) and a class weight formula based on the number of images contained in the class, which improves the performance of the model in the low-frequency class and the overall effect of the model by improving the degree of attention paid to the valid features and invalid ones and rebalancing the learning efficiency among the classes. We evaluated our method on an uterine MRI dataset and compared it with the ResUNet. WA-ResUNet increased Intersection over Union (IoU) in the low-frequency class (Nabothian cysts) by 21.87%, and the overall mIoU increased by more than 6.5%." @default.
• W4385655252 created "2023-08-09" @default.
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• W4385655252 date "2023-08-08" @default.
• W4385655252 modified "2023-09-27" @default.
• W4385655252 title "WA-ResUNet: A Focused Tail Class MRI Medical Image Segmentation Algorithm" @default.
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• W4385655252 doi "https://doi.org/10.3390/bioengineering10080945" @default.
• W4385655252 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37627829" @default.
• W4385655252 hasPublicationYear "2023" @default.
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