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,deepmultiple 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.