学习空间保持的层次表示用于数字病理学
Learning Spatial-Preserving Hierarchical Representations for Digital Pathology
AI总结：
本文提出SPAN框架，通过保留空间关系和计算分配，提升数字病理学图像的层次表示能力，通过两种变体在多个数据集上验证了其有效性。
摘要：
全滑片图像（WSI）由于其十亿像素分辨率和信息区域稀疏分布，带来了根本性的计算挑战。现有方法常独立处理图像块或以扭曲空间上下文的方式重塑它们，从而掩盖了WSI固有的层次金字塔表示。我们引入稀疏金字塔注意力网络（SPAN），一种层次框架，能够在保留空间关系的同时将计算分配给信息区域。SPAN直接从单尺度输入构建多尺度表示，使WSI数据的精确层次建模成为可能。我们通过两种变体：SPAN-MIL用于滑片分类，SPAN-UNet用于分割，展示了SPAN的通用性。在多个公开数据集上的全面评估表明，SPAN有效捕捉了层次结构和上下文关系。我们的结果提供了明确证据，表明架构归纳偏置和层次表示增强了滑片级和块级性能。通过解决WSI分析中的关键计算挑战，SPAN为计算病理学提供了有效的框架，并展示了大规模医学图像分析的重要设计原则。
英文摘要：
Whole slide images (WSIs) pose fundamental computational challenges due to their gigapixel resolution and the sparse distribution of informative regions. Existing approaches often treat image patches independently or reshape them in ways that distort spatial context, thereby obscuring the hierarchical pyramid representations intrinsic to WSIs. We introduce Sparse Pyramid Attention Networks (SPAN), a hierarchical framework that preserves spatial relationships while allocating computation to informative regions. SPAN constructs multi-scale representations directly from single-scale inputs, enabling precise hierarchical modeling of WSI data. We demonstrate SPAN's versatility through two variants: SPAN-MIL for slide classification and SPAN-UNet for segmentation. Comprehensive evaluations across multiple public datasets show that SPAN effectively captures hierarchical structure and contextual relationships. Our results provide clear evidence that architectural inductive biases and hierarchical representations enhance both slide-level and patch-level performance. By addressing key computational challenges in WSI analysis, SPAN provides an effective framework for computational pathology and demonstrates important design principles for large-scale medical image analysis.
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