Progressive attention module for segmentation of volumetric medical images.
Journal:
Medical physics
Published Date:
Jan 1, 2022
Abstract
PURPOSE: Medical image segmentation is critical for many medical image analysis applications. 3D convolutional neural networks (CNNs) have been widely adopted in the segmentation of volumetric medical images. The recent development of channelwise and spatialwise attentions achieves the state-of-the-art feature representation performance. However, these attention strategies have not explicitly modeled interdependencies among slices in 3D medical volumes. In this work, we propose a novel attention module called progressive attention module (PAM) to explicitly model the slicewise importance for 3D medical image analysis.