Evolutionary Attention Network for Medical Image Segmentation

Medical image segmentation is an active research topic to analyse medical images to find an organ or possible abnormalities in an image. Using a Convolutional Neural Network (CNN) is a successful technique for medical image segmentation. However, developing a CNN is a difficult task, especially when it includes complex structures, such as an attention mechanism. A CNN equipped with an attention mechanism is able to focus on a specific part of an image to extract a Region Of Interest (ROI), that can play a significant role to increase the accuracy of an image segmentation. Due to the difficulty of developing an attention network, in this paper, we introduce a new evolutionary technique to generate an attention network automatically for medical image segmentation. To the best of our knowledge, this is the first attempt to create an attention network using an evolutionary technique. To do this, a new encoding model is introduced to create a network topology, along with its training parameters, to ease the complexity of developing a CNN. Also, a Genetic Algorithm (GA) is applied to evolve the networks. To show the capability of the proposed technique, we used three publicly available medical segmentation datasets. The obtained results show that the proposed model can generate networks corresponding to each dataset, such that the developed networks have high performance for medical image segmentation.