Multi-scale conv-attention U-Net for medical image segmentation.

ABSTRACT: U-Net-based network structures are widely used in medical image segmentation. However, effectively capturing multi-scale features and spatial context information of complex organizational structures remains a challenge. To address this, we propose a novel network structure based on the U-Net backbone. This model integrates the Adaptive Convolution (AC) module, Multi-Scale Learning (MSL) module, and Conv-Attention module to enhance feature expression ability and segmentation performance. The AC module dynamically adjusts the convolutional kernel through an adaptive convolutional layer. This enables the model to extract features of different shapes and scales adaptively, further improving its performance in complex scenarios. The MSL module is designed for multi-scale information fusion. It

SUBMITTER: Pan P 

PROVIDER: S-EPMC11978844 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature