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

Abstract

Abstract 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.