EMCAH-Net: an effective multi-scale context aggregation hybrid network for medical image segmentation.

ABSTRACT:

Background

Pixel-level medical image segmentation tasks are challenging due to factors such as variable target scales, complex geometric shapes, and low contrast. Although U-shaped hybrid networks have demonstrated strong performance, existing models often fail to effectively integrate the local features captured by convolutional neural networks (CNNs) with the global features provided by Transformers. Moreover, their self-attention mechanisms often lack adequate emphasis on critical spatial and channel information. To address these challenges, our goal was to develop a hybrid deep learning model that can effectively and robustly segment medical images, including but not limited to computed tomography (CT) and magnetic resonance (MR) images.

Methods

We propose an effective h

SUBMITTER: Jin Y 

PROVIDER: S-EPMC11994538 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature