# MDA-Unet: A Multi-Scale Dilated Attention U-Net for Medical Image Segmentation
## 6 Conclusions  
In this paper, we proposed a novel deep learning model (MDA-Unet). The model leverages various computer vision modules to enhance the capability of U-Net model in capturing multi-scale information. In principle, we replaced the basic U-Net blocks with alternative residual blocks to provide a deeper model, ease the training process, and reduce the gradient vanishing problem. We integrated the spatial mechanism with a hybrid hierarchical dilated module, which has the following advantages: (1) It is constituted of different dilation rates to provide different receptive fields which captures multi-scale context information, where the small receptive field is needed to capture small objects and the large receptive field is beneficial for capturing large scale objects, and (2) it provides a dense region of interest prediction, compensating for the loss of semantic information induced by max-pooling operation. Furthermore, we precisely integrated the multi-scale spatial attention module into the skip connections to alleviate the disparity between encoder-decoder features and further highlight key areas at multiple scales and restrain irrelevant areas. Moreover, to enhance the encoder-decoder feature concatenation, we used the decoder features to guide the selection of low-level features. This is achieved by passing both encoder and decoder features to a channel attention mechanism and adding their channel attention map, which is multiplied by encoder features (coming from skip connection) to produce refined encoder features before concatenating with the corresponding decoder features. Incorporating these modifications, our module has overshadowed U-Net and its variants, ResDUnet, Attention U-Net, and U-Net3+ on two different medical datasets, lung CT and echocardiographic dataset. Both datasets have variability in the shape and size of the region of interest, where echocardiographic images were more challenging with a fuzzy boundary of the region of interest. However, our model has significantly improved manual segmentation with a minimal increase in trainable parameters on both datasets. Therefore, we believe that MDA-Unet has a great potential in helping physicians during their examination and improving the clinical workflow. In the future, it might be helpful to provide a more sparse and denser receptive field in HHDC. This can be achieved by incorporating a large convolution kernel in parallel \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dilation Rate (d)** & **DSC** & **Parameters** \\ \hline d = 1,2 & 95.3 \(\pm\) 4.2 & 7,828,681 \\ d = 1,2,3 & **98.3 \(\pm\) 2.4** & 7,830,793 \\ d = 1,2,5 & 97.2 \(\pm\) 3.1 & 7,830,793 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Segmentation results and trainable parameters for different values of the dilation rate. with dilated convolution. In addition, we wish to address the model performance for multiple trainings to analyze the interobserver variability. Furthermore, coupling the model with post-processing techniques, such as CRF as RNN, might help capture edge details accurately. Moreover, we further wish to define an appropriate search space to indicate which hyperparameters need to be optimized for effective performance on other medical images, such as MRI and mammogram images. Finally, we are interested in assessing the performance of the proposed modules across other state-of-the-art segmentation models, such as TransUnet, since the proposed modules can be used as plug-and-play modules for any CNN-based model. Conceptualization, A.A. and X.Y. ; methodology, A.A. and X.Y. ; software, A.A.; validation, A.A., X.Y. and T.L. ; formal analysis, A.A.; investigation, A.A.; resources, A.A. and X.Y. ; data curation, A.A., X.Y. and T.L. ; writing--original draft preparation, A.A.; writing--review and editing, X.Y. and T.L. ; visualization, A.A.; supervision, X.Y. and T.L. ; project administration, X.Y. and T.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.