SPAN: Unlocking Pyramid Representations for Gigapixel Histopathological Images

Whole slide images (WSIs) present fundamental computational challenges due to their gigapixel-scale resolutions and sparse, irregularly distributed informative regions. Conventional patch-based methods inevitably distort spatial relationships or treat patches as independent samples, while traditional attention mechanisms, designed for dense, uniformly distributed data, are computationally impractical for WSIs. To address these limitations, we propose a novel sparse-native computational framework that preserves exact spatial relationships, unlocking advanced modeling techniques and bridging a long-standing gap between WSI analysis and general vision. Based on this framework, we develop Sparse Pyramid Attention Networks (SPAN), incorporating a hierarchical sparse pyramid attention architecture with shifted windows that efficiently directs computational resources to informative regions. SPAN comprises two key modules: Spatial-Adaptive Feature Condensation, which progressively builds multi-scale representations from a single-scale input through sparse downsampling, and Context-Aware Feature Refinement, which captures long-range dependencies via shifted windows and global tokens. Evaluations on multiple public datasets demonstrate SPAN's superior performance over state-of-the-art methods, validating both our framework's effectiveness and SPAN's specific advantages in capturing contextual and hierachical representations that existing methods fundamentally cannot model. Our work establishes a new paradigm for WSI analysis that overcomes long-standing computational barriers. The code will be made publicly available upon publication.