Deep multiple instance selection

Multiple instance learning (MIL) assigns a single class label to a bag of instances tailored for some real-world applications such as drug activity prediction. Classical MIL methods focus on figuring out interested instances, that is, region of interests (ROIs). However, owing to the non-differentiable selection process, these methods are not feasible in deep learning. Thus, we focus on fusing ROIs identification with deep MILs in this paper. We propose a novel deep MIL framework based on hard selection, that is, deep multiple instance selection (DMIS), which can automatically figure ROIs out in an end-to-end approach. To be specific, we propose DMIS-GS for instance selection via gumbel softmax or gumbel top-k, and then make predictions for this bag without the interference of redundant instances. For balancing exploration and exploitation of key instances, we apply a cooling down approach to the temperature in DMIS-GS, and propose a variance normalization method to make this hyper-parameter tuning process much easier. Generally, we give a theoretical analysis of our framework. The empirical investigations reveal the proposed frameworks’ superiorities against classical MIL methods on generalization ability, positioning ROIs, and comprehensibility on both synthetic and real-world datasets.