A frequency-enhanced Vision Transformer with FDSA, FGMLP, WAFF, and FCSB modules delivers superior volumetric medical image segmentation performance and efficiency over prior state-of-the-art methods.
arXiv preprint arXiv:2209.15076 , year=
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GCNV-Net achieves state-of-the-art accuracy on multiple 3D medical segmentation benchmarks while cutting FLOPs by 56% and inference latency by 68% through dynamic nonvoid voxelization and geometric attention.
ImplantMamba combines CNN feature extraction with Mamba global modeling and a slope-coupled branch to predict implant position and angulation from surrounding dental textures.
citing papers explorer
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Geometrical Cross-Attention and Nonvoid Voxelization for Efficient 3D Medical Image Segmentation
GCNV-Net achieves state-of-the-art accuracy on multiple 3D medical segmentation benchmarks while cutting FLOPs by 56% and inference latency by 68% through dynamic nonvoid voxelization and geometric attention.
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ImplantMamba: Long-range Sequential Modeling Mamba For Dental Implant Position Prediction
ImplantMamba combines CNN feature extraction with Mamba global modeling and a slope-coupled branch to predict implant position and angulation from surrounding dental textures.