A 2.5D U-Net with pixel-wise cross-slice attention and skip attention gating achieves Dice 0.9535 on a private femur MRI dataset, outperforming compared 2D, 2.5D, and 3D baselines in full-scan evaluation.
A two-stage deep learning network for automated femoral segmentation in bilateral lower limb ct scans,
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XAG-Net: A Cross-Slice Attention and Skip Gating Network for 2.5D Femur MRI Segmentation
A 2.5D U-Net with pixel-wise cross-slice attention and skip attention gating achieves Dice 0.9535 on a private femur MRI dataset, outperforming compared 2D, 2.5D, and 3D baselines in full-scan evaluation.