Diagnose like a Clinician: Third-order Attention Guided Lesion Amplification Network for WCE Image Classification

摘要

Wireless capsule endoscopy (WCE) is a novel imaging tool that allows the noninvasive visualization of the entire gastrointestinal (GI) tract without causing discomfort to the patients. Although convolutional neural networks (CNNs) have obtained promising performance for the automatic lesion recognition, the results of the current approaches are still limited due to the small lesions and the background interference in the WCE images. To overcome these limits, we propose a Third-order Attention guided Lesion Amplification Network (TALA-Net) for WCE image classification. The TALA-Net consists of two branches, including a global branch and an attention-aware branch. Specifically, taking the high-level features in the global branch as the input, we propose a Third-order Attention (ToA) module to generate attention maps that can indicate potential lesion regions. Then, an Attention Guided Lesion Amplification (AGLA) module is proposed to deform multiple level features in the global branch, so as to zoom in the potential lesion features. The deformed features are fused into the attention-aware branch to achieve finer-scale lesion recognition. Finally, predictions from the global and attention-aware branches are averaged to obtain the classification results. Extensive experiments show that the proposed TALA-Net outperforms state-of-the-art methods with an overall classification accuracy of 94.72% on the WCE dataset.