# MDA-Unet: A Multi-Scale Dilated Attention U-Net for Medical Image Segmentation  
[PERSON]  
1 College of Computer Science, Arab Academy for Science and Technology (AAST) University, Smart Village, Cairo B12 577, Egypt 12 School of Computer Science, College of Science, University of Lincoln, Brayford Pool, Lincoln LN6 7 TS, UK 2  
[PERSON]  
3 School of Natural and Computing Sciences, Aberdeen University, Old Aberdeen Campus, Aberdeen AB24 3 UE, UK; [EMAIL_ADDRESS]  
[PERSON]  
1 College of Computer Science, Arab Academy for Science and Technology (AAST) University, Smart Village, Cairo B12 577, Egypt 12 School of Computer Science, College of Science, University of Lincoln, Brayford Pool, Lincoln LN6 7 TS, UK 2  
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prone to intra- and interobserver variability. Thus, an automated and accurate segmentation is mandatory for improving clinical workflow and supporting quick decision making for patient treatment. However, providing an automated segmentation model is still challenging for the following reasons: (1) The limited amount of annotated medical datasets mislead the understanding of whether a model performs better or over-fits under the limited amount of dataset; (2) medical datasets require image processing tasks that require prior knowledge to help direct the learning-based model; (3) the challenging nature of medical images with fuzzy boundaries, motion artifacts, intensity dissimilarity, and variability in the shape and size of the region of interest; and (4) the model's generalizability, where a trained model fails on a new dataset due to the different equipment used for the same medical modality and the various image-acquisition protocols. These challenges have made the automated segmentation problem remain an attractive area of research. In this paper, in parallel to appreciating the capabilities of U-Net, we argue that its architecture may lack some criteria which limit its efficiency in segmenting challenging medical datasets across different modalities with variability in the shape and size of the region of interest. Consequently, based on recent advancements in deep computer vision, we propose a multi-scale dilated attention segmentation model named MDA-Unet. The model leverages its strength from U-Net architecture and various network modules, which consider network depth, multi-scale feature extraction, and distinguishable features through attention mechanisms. The main contributions of this paper are summarized as follows:  
* Based on potential scopes of improvement, we propose a multi-scale deep learning segmentation model with a slight increase in the number of trainable parameters in comparison with the basic U-Net model. * We increase the network depth by utilizing residual blocks instead of the basic U-Net blocks, which deepens the network using identity shortcut connections. Those connections help propagate the low fine details, ease the training process, and reduce the gradient vanishing problem.