Lightweight Medical Image Segmentation

Lightweight medical image segmentation focuses on developing efficient deep learning models for accurate image analysis while minimizing computational demands and model size. Current research emphasizes adapting U-Net architectures and incorporating techniques like large kernels, attention mechanisms, and optimized skip connections to improve global context capture and feature fusion without sacrificing performance. This area is crucial for deploying accurate segmentation tools on resource-constrained devices, enabling faster processing and wider accessibility of advanced medical image analysis in clinical settings.