Re-calibrated Attention-Based Deep Learning Technique for Dermoscopic Lesion Segmentation

Nowadays, the attention mechanism is widely used in image segmentation. Recently many attention gate (AG) based models have gained interest for use in the delineation processes. However, adding only AG in the models can sometimes produce fallacious results due to irregular lesion boundaries and illumination variation conditions. To address these issues, we propose two novel methods, Attention FocusNet and SE-Attention U-Net. Attention FocusNet focuses on the use of AG to get much improved hierarchical attention maps and lowers the details of irrelevant information in the model to create a segmented image mask. SE-Attention U-Net, on the other hand, re-calibrates the weights of the feature vector and pass it through AG to focus on relevant information and suppressing any superficial information. We experimented on three medical image datasets, which are ISIC 2017, ISIC 2018 and HAM10000. Validation results show that the proposed methods outperform the state-of-the-art methodologies and obtained better segmentation accuracy.