Medical Image Generation via Feature Fusion and Attention Mechanisms

Deep learning models rely on a large amount of labeled data, but the model performance is limited in medical imaging due to strict privacy protection and scarcity of labeled data. Generative models can alleviate the problem of data scarcity to some extent. However, the complexity of medical images leads to missing texture details and poor semantic consistency of the generated content, resulting in weak generalization ability. To solve the above problems, we propose an improved generative model, which employs a multilevel feature fusion strategy to enhance semantic accuracy, introduces a multi-head self-attention mechanism to capture texture and details, and designs a multi-scale discriminator to enhance global structure and local detail performance. Experiments show that our method effectively improves the quality of medical image generation and enhances the data usability and diagnostic value.