RIIS-DenseNet: Rotation-Invariant and Image Similarity Constrained Densely Connected Convolutional Network for Polyp Detection

Colorectal cancer is the leading cause of cancer-related deaths. Most colorectal cancers are believed to arise from benign adenomatous polyps. Automatic methods for polyp detection with Wireless Capsule Endoscopy (WCE) images are desirable, but the results of current approaches are limited due to the problems of object rotation and high intra-class variability. To address these problems, we propose a rotation invariant and image similarity constrained Densely Connected Convolutional Network (RIIS-DenseNet) model. We first introduce Densely Connected Convolutional Network (DenseNet), which enables the maximum information flow among layers by a densely connected mechanism, to provide end-to-end polyp detection workflow. The rotation-invariant regularization constraint is then introduced to explicitly enforce learned features of the training samples and the corresponding rotation versions to be mapped close to each other. The image similarity constraint is further proposed by imposing the image category information on the features to maintain small intra-class scatter. Our method achieves an inspiring accuracy 95.62% for polyp detection. Extensive experiments on the WCE dataset show that our method has superior performance compared with state-of-the-art methods.