Reinforcement Learning-guided Discriminative Region Sampling in Histopathology

With the widespread adoption of digital pathology, computational pathology has facilitated the transition from traditional manual interpretation to intelligent-assisted diagnosis through the analysis of whole-slide images (WSIs). Nevertheless, WSIs are gigapixel in size and contain sparsely distributed diagnostically relevant information, while large portions of tissue may lack diagnostic relevance or even confound the model's decision-making. In addition, the heterogeneity of tissue structures scatters diagnostic features, necessitating models that capture long-range dependencies and integrate contextual information effectively. In this paper, we propose a novel reinforcement learning-guided Discriminative Region Sampling Framework (DRSF) for weakly supervised histopathology WSI classification. Central to DRSF is the proposed discriminative region sampling strategy, in which a reinforcement learning (RL)-based policy network sequentially samples the patches deemed most diagnostically relevant, thereby reducing redundant or interfering information. The classification module utilizes a bidirectional Mamba backbone to effectively model long-range semantic dependencies among dispersed sampled patches, while accommodating variable-length input sequences. Experimental results on WSI datasets of different staining types demonstrate that DRSF outperforms representative multiple-instance learning (MIL) frameworks. Moreover, DRSF enables direct visualization of the intermediate results of selected patches and sampling trajectories, thereby assisting pathologists in rapidly locating key regions and enhancing the interpretability of automated diagnosis.