DMA-Net: Dual multi-instance attention network for X-ray image classification

Security inspection has been playing a critical role in protecting public space from safety threats. As object detection is a fundamental and mature research filed, it still suffers from numerous challenges such as scale, viewpoint and intra-class variance of X-ray images. The main reasons are, mis-modeling of actual security inspection as well as insufficient effective X-ray samples. To this end, the X-ray inspection task is reconsidered by predicting the dangerous attributes without boxes and categories, a Dual Multi-instance Attention network (DMA-Net) is proposed in this paper to mine the key-instance from both patch and proposal branch. In patch-level multi-instance attention pooling, Recursive Attention Pyramid (RAP) and Spatial-Channel Attention (SCA) are proposed to effectively learn discriminative representation from hierarchical features. Meanwhile, the proposal-level multi-instance attention pooling selectively emphasizes interdependent of crucial regions. The features of dual pooling layers are fused to predict dangerous attributes of X-ray inspection images directly. Further, this paper contributes a large-scale and high-quality X-ray image dataset from railway stations, named Railway Station X-ray (RSXray). Enormous experiments on RSXray, GDXray and SIXray demonstrate that the proposed DMA-Net achieves surprising performance with dual multi instance learning diagrams and attention mechanism. The superiority of the employed framework for applications in real world scenarios are also verified.