LightAWNet: Lightweight adaptive weighting network based on dynamic convolutions for medical image segmentation.

Purpose: The complexity of convolutional neural networks (CNNs) can lead to improved segmentation accuracy in medical image analysis but also results in increased network complexity and training challenges, especially under resource limitations. Conversely, lightweight models offer efficiency but often sacrifice accuracy. This paper addresses the challenge of balancing efficiency and accuracy by proposing LightAWNet, a lightweight adaptive weighting neural network for medical image segmentation.

CeLNet: a correlation-enhanced lightweight network for medical image segmentation.

. Convolutional neural networks have been widely adopted for medical image segmentation with their outstanding feature representation capabilities. As the segmentation accuracy gets constantly updated, the complexity of networks increases as well.