Tissue segmentation requires pixel-level labels that are expensive to obtain in histopathology. This paper develops an unsupervised representation learning framework that moves from global to local contrastive objectives so that the learned features become useful for fine-grained tissue discrimination. By encoding multi-granularity views without annotations, it improves segmentation quality under limited-label conditions.