TL;DR
GTUNet is proposed, a novel hybrid framework that synergistically integrates Graph Attention Networks, Transformers, and UNet for robust 3D medical image segmentation and significantly outperforms existing methods in ASSD, highlighting its precision in delineating vascular boundaries.
TransCat, a hybrid CNN-Transformer architecture for medical image segmentation, is proposed and an extended deformable attention mechanism with attentive value identification is developed, to control the computational burden caused by the enlarged token set.
Medical image segmentation requires high accuracy and robustness, yet practical commercial deployment also demands privacy preservation and computational efficiency. In this context, the U-Net architecture, which can be inherently decoupled into independent encoder and decoder components, serves as a natural commercial...
A hybrid CNN-Transformer architecture guided by channel attention, referred to as CACFormer, for 3D medical image segmentation, and a novel linear Transformer variant that integrates a linear attention mechanism with tanh activation are introduced.
To overcome the architectural mismatch inherent in existing hybrid networks, a novel Scaling Feature Pyramid (SFP) is proposed, which effectively bridges the single-scale 3D Vision Transformer (ViT) encoder and the multi-scale CNN decoder by transforming the ViT's output into a hierarchical feature pyramid, ensuring th...
A Parallel Mamba Dual-U Network that adopts a cascaded dual U-Net encoder-decoder for two-stage “coarse-to-fine” segmentation refinement and outperforms state-of-the-art methods on most core metrics, verifying its effectiveness and robustness for complex medical image segmentation.
DMFU-Net, a spatial–frequency attention-guided segmentation framework built upon a U-Net-style architecture, is proposed, demonstrating its superior reliability in challenging small-lesion cases and its potential value for downstream clinical quantitative analysis.
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