Fourier fusion and dual-path attention enhancement network for medical image segmentation

GLNet: global-to-local aware hybrid framework for medical image segmentation

GLNet adopts a dual-branch encoder that combines a CNN-based Local Detail Perception Branch with a Mamba-based Global Context Modeling Branch, enabling the joint extraction of fine-grained local features and long-range semantic representations.

TSPFusion: Tri-stream and prototype network for learning detail-semantic fusion in medical image segmentation

A tri-stream interaction paradigm replacing symmetric skip connections with directional fusion among semantic, spatial, and decoder-propagated streams at each decoding stage, and a Global Prototype Bank that captures dataset-level anatomical regularities via attention-based retrieval and gated EMA updates, providing pe...

Cross-layer semantic alignment and context enhancement network for medical image segmentation

Experiments demonstrate that the proposed cross-layer semantic alignment mechanism outperforms mainstream approaches in terms of Dice, HD95, and Intersection over Union metrics, validating the effectiveness of the cross-layer semantic alignment mechanism for complex medical image segmentation tasks.

Vessel Segmentation Based on a Channel-Attention U-Net Algorithm

Results indicate that CA-UNet provides an effective balance of segmentation accuracy, stability, and computational efficiency for vessel segmentation.

MAF-Net: multi-scale attention fusion network for pancreas segmentation

DD-Net: a dual-domain progressive fusion network enhanced by high–low frequency separation for lung nodule segmentation

Introduction Precise segmentation of pulmonary nodules plays a vital role in differentiating benign from malignant lesions and in tracking their progression over time, however, the substantial variability of nodules in size, morphology, and texture frequently results in either excessive or insufficient segmentation. Me...