Multi‐Head Convolution Module With Dynamic Feature Fusion for Medical Image Segmentation

ABSTRACT Transformers have achieved remarkable success in medical image segmentation due to their large receptive fields and global context extraction capabilities. However, convolutional neural networks (CNNs) face limitations with fixed‐size kernels, hindering their ability to capture multi‐scale features. To address these challenges, we propose two novel modules: the multi‐head channel mixed convolution (MHCMC) module, which enhances feature extraction by expanding the receptive field and utilizing channel attention, and the dynamic feature aggregation (DFA) module, which adaptively prioritizes crucial spatial features based on global information. Additionally, we introduce the evolutionary hybrid network (EHN) to simulate the transition from local to global dependency capture. The MHCMC and DFA modules are first fused to construct the multi‐head DFA (MHDFA) module. Subsequently, the MHDFA and EHN modules are sequentially stacked to form the encoder, which we denote as the multi‐head dynamic aggregation transformer (MDAT). By further integrating MDAT into the U‐Net architecture, we obtain the proposed MDAT‐Net. Experimental results show that MDAT‐Net outperforms other state‐of‐the‐art models in three medical image segmentation tasks: the liver tumor segmentation benchmark (LiTs2017), CVC LinicDB, and the automated cardiac diagnosis challenge (ACDC).