Triple ANet: Adaptive Abnormal-aware Attention Network for WCE Image Classification

Accurate detection of abnormal regions in Wireless Capsule Endoscopy (WCE) images is crucial for early intestine cancer diagnosis and treatment, while it still remains challenging due to the relatively low contrasts and ambiguous boundaries between abnormalities and normal regions. Additionally, the huge intra-class variances, alone with the high degree of visual similarities shared by inter-class abnormalities prevent the network from robust classification. To tackle these dilemmas, we propose an Adaptive Abnormal-aware Attention Network (Triple ANet) with Adaptive Dense Block (ADB) and Abnormal-aware Attention Module (AAM) for automatic WCE image analysis. ADB is designed to assign one attention score for each dense connection in dense blocks and to enhance useful features, while AAM aims to adaptively adjust the respective field according to the abnormal regions and help pay attention to abnormalities. Moreover, we propose a novel Angular Contrastive loss (AC Loss) to reduce the intra-class variances and enlarge the inter-class differences effectively. Our methods achieved 89.41% overall accuracy and showed better performance compared with state-of-the-art WCE image classification methods. The source code is available at https://github.com/Guo-Xiaoqing/Triple-ANet.