# MDA-Unet: A Multi-Scale Dilated Attention U-Net for Medical Image Segmentation
## 2 Related Work
[16] proposed Attention U-Net for segmenting computed tomographic (CT) abdominal images. They proposed a spatial attention gate (AG) and integrated it into the U-Net skip connections. Their model was efficient in locating objects and has outperformed the basic U-Net model. However, it still lacks the consideration of high-level semantics since their attention mechanism focuses only on shallow encoder features. In addition, it disregards capturing multi-scale features, which are highly effective in segmenting medical objects with variability in size and shape. Moreover, [PERSON] et al. [17] has integrated a spatial attention mechanism in the U-Net bottleneck to extract informative features which improved the network performance in segmenting vessels from retinal fundus images. Looking from another perspective, [PERSON] et al. [18] developed a channel-attention network based on U-Net to segment esophagus and esophageal cancer lesions from CT images. Their model provided promising results by filtering out less informative features and highlighting important ones. Most recently, although few studies [12; 19; 20; 21] have proposed to utilize both attention aspects. However, according to the literature, the idea of developing a multi-scale feature attention module was never addressed. In addition, the fusion strategy and the location of both attention mechanisms is still uncertain. Multiscale semantic information plays a crucial role in achieving a high performance for a segmentation network. Earlier methods usually use pooling operations or increase the kernel size to a provide larger receptive field of an output neuron. However, increasing the kernel size requires many computations and increases the model complexity [22]. In addition, increasing the number of pooling layers provide a reduced feature map size, which potentially leads to loss of useful spatial information and poses challenges to upsample the segmentation output back to its original resolution. On the other hand, if the segmentation results are achieved at the larger resolution from the earlier layers, we will not be able to make use of the higher-level semantics for better understanding [23]. Therefore, to capture multi-scale information, dilated convolution have been utilized in the atrous spatial pyramid pooling (ASPP) module developed in DeeplabV3 [24], which is inspired by spatial pyramid pooling (PSP) [25]. The ASPP module effectively increases the receptive field by using different dilation rates in parallel, which is beneficial in capturing objects and image contexts at multiple scales. However, for some applications, to achieve a large enough receptive field, the ASPP has to employ a very large dilation rate (i.e., \(\text{d}\geq 25\)). This increased dilation rate causes discontinuity in computation because the neighboring units in the output are computed from separate units in the input, which causes inconsistency in small local details and less modeling power [26]. Moreover, the ASPP module brings high computation cost because it demands the input feature maps to have a large number of channels (i.e., 2048) [27]. Inspired from ASPP, DenseASPP [28] was proposed, which integrates wider contextual information with increasing the dilation rate layer by layer. However, in addition to holding much computation cost, a major drawback in this method is the inattentiveness to local fine details, since it models contextual information from high-level features only, without paying attention to recovering the semantic details of the input. The above-mentioned methods show that U-Net is the most widely used model in medical image segmentation. They also highlight how attention mechanisms and multi-scale feature extraction methods efficiently boost the segmentation performance.