Whole Slide Images (WSIs) are crucial for modern pathological diagnosis, yet
their gigapixel-scale resolutions and sparse informative regions pose
significant computational challenges. Traditional dense attention mechanisms,
widely used in computer vision and natural language processing, are impractical
for WSI analysis due to the substantial data scale and the redundant processing
of uninformative areas. To address these challenges, we propose
Memory-Efficient Sparse Pyramid Attention Networks with Shifted Windows (SPAN),
drawing inspiration from state-of-the-art sparse attention techniques in other
domains. SPAN introduces a sparse pyramid attention architecture that
hierarchically focuses on informative regions within the WSI, aiming to reduce
memory overhead while preserving critical features. Additionally, the
incorporation of shifted windows enables the model to capture long-range
contextual dependencies essential for accurate classification. We evaluated
SPAN on multiple public WSI datasets, observing its competitive performance.
Unlike existing methods that often struggle to model spatial and contextual
information due to memory constraints, our approach enables the accurate
modeling of these crucial features. Our study also highlights the importance of
key design elements in attention mechanisms, such as the shifted-window scheme
and the hierarchical structure, which contribute substantially to the
effectiveness of SPAN in WSI analysis. The potential of SPAN for
memory-efficient and effective analysis of WSI data is thus demonstrated, and
the code will be made publicly available following the publication of this
work.

提出了一种名为 Memory-Efficient Sparse Pyramid Attention Networks with Shifted Windows (SPAN) 的方法，通过引入稀疏金字塔注意力架构和移动窗口的机制来处理具有庞大数据规模和冗余处理的无用区域的病理学 WSI 图像分析，以实现内存效率化和 WSI 数据的精确建模。