Exploiting Saliency in Attention Based Convolutional Neural Network for Classification of Vertical Root Fractures

Zhenxing Xu, Peng Wan, Gulibire Aihemaiti, Daoqiang Zhang

Abstract: Cone-beam computed tomography (CBCT) is widely used in clinical diagnosis of vertical root fractures (VRFs) which presents as crack on the teeth. However, manually checking the VRFs from a larger number of CBCT images is time-consuming and error-prone. Although the Convolutional Neural Networks (CNN) have achieved unprecedented progress in natural image recognition, end-to-end CNN is unsuitable to identify VRFs due to crack appears to be multi-scales and their complex relationships with surroundings tissues. We proposed a novel Feature Pyramids Attention Convolutional Neural Network (FPA-CNN), which incorporates saliency mask and multi-scale feature to boost the classification performance. Saliency map is viewed as spatial probability map where a person might look first to make a discriminative conclusion. Therefore it plays a role of high-level hint to guide the network focusing on the discriminative region. Experimental results demonstrate that our proposed FPA-CNN overcomes the challenge arised from multi-scale crack and complex contextual relationships.