Interactive segmentation offers greater clinical potential in computational pathology compared to traditional automatic segmentation. By incorporating interactive input, it addresses the limitations of fully automatic segmentation models, which often fail to meet pathologists' requirements and rely heavily on large-scale, pixel-level annotated datasets. However, current interactive segmentation methods struggle to balance interaction cost and segmentation performance, and they fail to adapt effectively to slide-level segmentation, a task that is even more crucial in routine pathology analysis. In this study, we propose a Prototype-Guided Interactive Segmentation (ProGIS) framework for pathological image segmentation, designed to deliver precise segmentation results efficiently with minimal interaction signals. ProGIS identifies all same-type tissue connected components in a single interaction and supports multi-class segmentation without predefined categories during inference. Moreover, ProGIS can be easily adapted for slide-level interactive segmentation. Specifically, ProGIS consists of three modules: Prototype Initialization, Prototype Navigation, and Local Refinement. First, the Prototype Initialization module identifies categorical prototypes, which are then utilized in the Prototype Navigation module to identify all tissue connected components belonging to the same type. The local refinement module further refines the segmentation results using detailed correction signals to ensure the accuracy of challenging-to-distinguish regions. We evaluate our framework on two regions of interest level and two slide-level pathological segmentation datasets, achieving new state-of-the-art performance with fewer interactions than existing methods. Our code is available at https://github.com/JSGe-AI/ProGIS.

Modeling clinical dynamics requires capturing how patient states evolve across visits while also accounting for the heterogeneous clinical signals present within each encounter. Existing approaches often emphasize temporal progression or intra-visit structure in isolation, leading to incomplete representations of patient trajectories and limited generalizability in realworld settings. We propose a bi-perspective clinical dynamics framework that jointly models longitudinal evolution and finegrained visit-level structure as two complementary axes of patient state formation. This design produces a unified trajectory representation that preserves long-range dependencies while remaining sensitive to intra-visit diagnostic, procedural, and treatmentrelated cues. To further stabilize prediction under data sparsity and heterogeneous patient profiles, the framework incorporates an optional similarity-guided refinement module that leverages relational patterns across historical cases without increasing inference overhead. Evaluations on two large critical-care datasets demonstrate that the proposed approach consistently yields stronger predictive behavior, enhanced robustness under sparse and irregular trajectories, and substantially improved computational efficiency compared with widely adopted architectures. These results highlight the effectiveness of structuring clinical event prediction around complementary temporal and contextual perspectives, offering a scalable pathway toward next-generation clinical decision-support systems.