Employing Attention-Based Learning For Medical Image Segmentation

Automated medical image analysis is a non-trivial task due to the complexity of medical data. With the advancements made on Computer Vision through the golden era of Deep Learning, many models which rely on Deep Convolutional Networks have emerged in the Medical Imaging domain and offer important contributions in automating the analysis of medical images. Based on recent literature, this work proposes the adaptation of visual attention gates in Fully Convolutional Encoder-Decoder networks in the Medical Image Segmentation task. Appropriate data pre-processing is performed in the cases of 2-dimensional and 3-dimensional data in order to serve them as proper inputs in conventional and attention-gated Deep Convolutional Networks that try to identify classes in pixel and voxel level respectively. Attention gates can be easily integrated in the conventional networks, that would improve their performance. We present the specific mechanics of attention gates, conduct experiments and analyse our derived results. Finally, based on the latter, we provide our opinion and intuition on how this work can be further expanded towards new research directions.