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- W3165806379 abstract "Due to their high capacity in capturing 3D spatial information, 3D Fully Convolutional Neural Networks (3D FCNs), especially 3D U-Net, are prevalent for volumetric medical image segmentation. However, 3D convolutions are much more computationally complex than 2D convolutions and thus, are more prone to overfitting. This paper proposes Collaborative Multi-View convolutions (CMV convs) that can keep the model complexity similar to those employing 2D convolutions while capturing the 3D spatial context like 3D convolutions. Specifically, CMV convs simultaneously extract information from three orthogonal views with three parameter-shared 2D convolutions. A Global-Guided Gating mechanism (3G) is further designed that selectively passes information from CMV convs to the next stage. Combined with 3G, a CMV conv become a G-CMV conv that constitutes a plug-and-play module, which can be easily integrated into various 3D CNNs for image segmentation. Extensive experiments utilizing BraTS18 dataset have been conducted. Our method achieves competitive results compared to state-of-the-art methods with over 10× fewer parameters than 3D-UNet." @default.
- W3165806379 created "2021-06-07" @default.
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- W3165806379 date "2021-04-13" @default.
- W3165806379 modified "2023-09-27" @default.
- W3165806379 title "Collaborative Multi-View Convolutions With Gating For Accurate And Fast Volumetric Medical Image Segmentation" @default.
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- W3165806379 doi "https://doi.org/10.1109/isbi48211.2021.9433787" @default.
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