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- W4313298774 abstract "Abstract Background : Glioma is the most common brain tumor disease. Magnetic resonance can help the clinical diagnosis according to the location of the glioma and the degree of malignancy, in which the segmentation of glioma site plays an important role for clinicians. The work of manual segmentation is very time-consuming and cumbersome, therefore automatic and efficient segmentation methods are very necessary. Methods : This paper proposed an AIUNet to give a more efficient segmentation of glioma, where a new block---Attention based Inception Block (AI Block), combining convolution and Self-Attention, is introduced. This module combines the smaller receptive field of convolution with the larger receptive field of Self-Attention, so as to extract more diverse feature maps to meet the needs of refined segmentation, and some deformations of AI block will be introduced and applied to the segmentation of glioma lesion. The AIUNet image segmentation network is constructed by merging AI block with the U-shaped network, this network has excellent segmentation performance, as well as low amount of parameters and calculation. Moreover, a loss function combined with GHM loss and Dice loss is plugged into the network, thereby improving the robustness of the network. Results : Experiments show that the proposed network can improve segmentation effects comparing with state-of-the-art methods." @default.
- W4313298774 created "2023-01-06" @default.
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- W4313298774 date "2022-12-29" @default.
- W4313298774 modified "2023-09-28" @default.
- W4313298774 title "AIUNet: A Medical Image Segmentation Network Composed of Attention and Inception Blocks" @default.
- W4313298774 doi "https://doi.org/10.21203/rs.3.rs-2261492/v1" @default.
- W4313298774 hasPublicationYear "2022" @default.
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