MSR-UNet: enhancing multi-scale and long-range dependencies in medical image segmentation.

ABSTRACT: Transformer-based technology has attracted widespread attention in medical image segmentation. Due to the diversity of organs, effective modelling of multi-scale information and establishing long-range dependencies between pixels are crucial for successful medical image segmentation. However, most studies rely on a fixed single-scale window for modeling, which ignores the potential impact of window size on performance. This limitation can hinder window-based models' ability to fully explore multi-scale and long-range relationships within medical images. To address this issue, we propose a multi-scale reconfiguration self-attention (MSR-SA) module that accurately models multi-scale information and long-range dependencies in medical images. The MSR-SA module first divides the attention heads

SUBMITTER: Wang S 

PROVIDER: S-EPMC11623095 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature