MA-Unet: An improved version of Unet based on multi-scale and attention mechanism for medical image segmentation
This work provides an incremental improvement to U-Net architectures for medical image segmentation, potentially benefiting clinicians and researchers in medical diagnostics.
The paper addresses shortcomings in standard U-Net models for medical image segmentation, specifically semantic differences in skip connections, ineffective modeling of remote feature dependencies, and ignored multi-scale global context. The proposed MA-Unet integrates attention gates, attention mechanisms for global dependencies, and multi-scale predictive fusion, achieving better segmentation performance with fewer parameters compared to state-of-the-art networks.
Although convolutional neural networks (CNNs) are promoting the development of medical image semantic segmentation, the standard model still has some shortcomings. First, the feature mapping from the encoder and decoder sub-networks in the skip connection operation has a large semantic difference. Second, the remote feature dependence is not effectively modeled. Third, the global context information of different scales is ignored. In this paper, we try to eliminate semantic ambiguity in skip connection operations by adding attention gates (AGs), and use attention mechanisms to combine local features with their corresponding global dependencies, explicitly model the dependencies between channels and use multi-scale predictive fusion to utilize global information at different scales. Compared with other state-of-the-art segmentation networks, our model obtains better segmentation performance while introducing fewer parameters.