MAGNet: A Convolutional Neural Network with Multi-Scale and Global Attention Modules for Medical Image Segmentation

In this paper, we propose a novel convolutional neural network called MAGNet that employs multi-scale and global attention mechanisms for the task of medical image segmentation. This network is shown effectively to handle the segmentation task of an image of a given modality provided the network is suitably trained using a training set of the same modality. Experiments are performed to train the proposed network using three different training sets of images (CT, colonoscopy, and non-mydriatic 3CCD images), each acquired from a different imaging technique, resulting in three different trained models. The three trained models are tested on the respective test sets. Each model is shown to significantly outperform the state-of-the-art networks in terms of intersection over union, dice coefficient, and accuracy.