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.
SwinUNETR model with 32x32x32 patch sampling achieves DSC of 0.868 for LVCP segmentation in MS, outperforming UXNET with 99% lower computation.
citing papers explorer
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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.