Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting

ArXi:2511.19953v2 Announce Type: replace Accurate nuclear instance segmentation is a pivotal task in computational pathology, ing data-driven clinical insights and facilitating downstream translational applications. While large vision foundation models have shown promise for zero-shot biomedical segmentation, most existing approaches still depend on dense supervision and computationally expensive fine-tuning. Consequently