# MDA-Unet: A Multi-Scale Dilated Attention U-Net for Medical Image Segmentation
## 2 Related Work  
Most of the improvements in biomedical image segmentation are based on the U-Net model; this is due to the efficiency and simplicity of its architecture. However, it still has some drawback that limits its capabilities. Recently, some extension networks have been proposed to enhance its performance on medical image. For example, [PERSON] et al. [4] proposed U-Net++, which has provided good results for segmenting polyps, liver, brain tumor, and cell nuclei from different modalities. The structure of U-Net++ is composed of encoder and decoder sub-networks connected through nested and dense skip connections to aggregate multi-scale features [5]. Despite its superior performance, the main drawback in U-Net++ is that it involves complex and heavy computations for a large number of intermediate convolution operations. Moreover, [PERSON] et al. [6] introduced the U-Net3+ architecture, which is a full-scale connected U-Net with deep supervision making maximum use of the feature maps in full scales. The model was trained on liver CT images and surpassed state-of-the-art methods; however, this superior performance is accompanied by a large number of trainable parameters in comparison with the basic U-Net model. In addition, [PERSON] et al. [7] have introduced R2U-Net, which improves upon U-Net by embedding recurrent residual convolution blocks to train the network deeper. However, their model holds a large number of parameters (almost double U-Net) and is liable to over-fit when trained on a small sample of medical images. Another variant of U-net was proposed by [PERSON] et al. [8], which integrates a convolutional DropBlocks to suppress less informative features and alleviate the over-fitting problem. Their model has outperformed the basic U-Net model (with and without dropout) on retinal vessel images. However, despite the simplicity of their model, it still lacks capturing multi-scale information. Finally, [PERSON] et al. [9] proposed a left ventricle segmentation model based on U-Net, named ResDUnet. In their model, the feature map at each layer is simultaneously passed through residual blocks and dilated convolution; they aimed at enlarging the receptive field at each layer to capture global context information using a fixed sized dilation rate at each layer, which is still insufficient to extract multi-scale features from each layer. Attention mechanisms have led to a new research direction for medical image segmentation. These mechanisms leverage global information to highlight informative features and suppress less informative ones. This is performed through aggregating spatial features to focus on 'where' exactly a region of interest is, using a spatial attention mechanism, or aggregating channel features which focus on 'what' are the meaningful features to represent. In both cases, they suppress unrelated features, increase the feature representation power, and boost the network performance [10]. Accordingly, several studies focused on developing attention mechanisms to further promote the feature's representation power. [PERSON] et al. [11] proposed a model based on a squeeze and excitation unit which investigates the interdependencies among various channels from a convolution output. This unit performs global average pooling to emphasize useful channels and suppress useless ones. Their model has outperformed other models in ILSVRC 2017 for image classification. In addition, [PERSON] et al. [12] proposed an interesting attention mechanism, called convolution block attention module (CBAM), a self-attention mechanism that aggregates spatial and channel information for efficient feature refinement. Later on, they proposed a bottleneck attention module (SAM) [13], which proposed integrating dilated convolution into the spatial attention mechanism to facilitate constructing an effective spatial map. Moreover, other attention mechanisms were proposed in [14; 15]. For medical image segmentation models based on attention mechanisms, [PERSON] et al.