Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentation

Understand

Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been further designed to capture global contextual information for performance enhancement.

• However, the large size of 3D volume images poses a great computational challenge to traditional attention methods.
• In this paper, we propose a folded attention (FA) approach to improve the computational efficiency of traditional attention methods on 3D medical images.
• The main idea is that we apply tensor folding and unfolding operations with four permutations to build four small sub-affinity matrices to approximate the original affinity matrix.