PathHunter: Hierarchical Evidence Acquisition for Weakly Supervised WSI Classification

Histopathology remains the gold standard for definitive disease diagnosis, yet locating decisive evidence within gigapixel whole-slide images (WSIs) under slide-level supervision is difficult. Most weakly supervised pipelines still treat diagnosis as passive bag aggregation, even though clinical reading is inherently sequential and multi-scale. We propose PathHunter, a hierarchical reinforcement learning (HRL) framework that reframes weakly supervised WSI diagnosis as sequential evidence acquisition using visual features alone. A high-level Manager selects candidate regions of interest (ROIs), while a low-level Worker retrieves discriminative patches within each ROI, transforming passive aggregation into active evidence construction. PathHunter is trained in a weakly supervised loop that also acts as a curriculum over action-space complexity: Stage1 learns static evidence aggregation with MIL, Stage2 learns local evidence refinement from pseudo-expert rankings, and Stage3 learns full-slide evidence acquisition by jointly optimizing the Manager, the Worker, and the downstream MIL classifier on policy-selected trajectories. To improve credit assignment, we combine step-wise rewards from gains in label-consistent confidence with instance-score-based reward shaping. Across Camelyon16 and TCGA-NSCLC, PathHunter consistently improves diverse backbone-head combinations, with the largest gains on sparse-evidence binary detection and smaller but still mostly stable gains on harder morphology-driven classification. On Camelyon16, the improvement reaches +27.21% AUC for a weak Stage 1 pipeline; on PANDA, the same training recipe improves weighted kappa on all evaluated backbone-domain pairs. PathHunter additionally yields interpretable coarse-to-fine trajectories that expose how a compact evidence set is constructed during inference. Overall, the results indicate that hierarchical evidence acquisition is most beneficial when slide labels depend on sparse decisive regions, while broader morphology tasks benefit mainly through cleaner evidence aggregation. Anonymous code is https://anonymous.4open.science/r/pathhunter-08F1/README.md.