用单张高倍切片实现多尺度分析，提升病理图像诊断效率
Enabling Progressive Whole-slide Image Analysis with Multi-scale Pyramidal Network
- 通过网格重映射与粗粒度引导网络，仅用高倍输入实现渐进式多尺度分析
- 在5个临床任务上均提升分类性能，比基线平均提升1.8%~3.2%准确率
- 轻量级模块可无缝接入现有模型，适合医学影像研究者快速部署
多实例学习（MIL）广泛应用于计算病理学，多尺度特征对捕捉细胞细节和组织结构至关重要。然而，现有方法通常依赖固定多倍率输入或计算开销大的架构。随着预训练基础模型成为特征提取主流并推动轻量化模型发展，本文重新思考并探索更高效的多尺度MIL方法。提出多尺度金字塔网络（MSPN），一个可即插即用的注意力型MIL模块。MSPN仅需单个高倍率输入，即可实现渐进式全切片分析，包含（1）基于网格的重映射，将高倍特征聚合为具有空间感知的粗粒度特征图；（2）粗粒度引导网络（CGN），用于学习粗粒度上下文。我们在5个临床相关任务上，以2个基础模型和4个注意力框架为基线，验证MSPN作为附加模块的有效性。结果表明，无论配置或任务如何，MSPN均显著提升MIL表现，且模型轻量、易集成。
原文摘要 · Abstract (English)
Multiple-instance Learning (MIL) is commonly used for computational pathology (CPath), where multi-scale features are essential for capturing both fine cellular details and broad tissue architecture. However, existing multi-scale MIL approaches typically rely on the inflexible multi-magnification inputs or the computationally expensive architectures. As pre-trained foundation models (FMs) become the trend for feature extraction and boost lightweight models, we rethink and explore a more efficient multi-scale MIL method. In this paper, we propose the Multi-scale Pyramidal Network (MSPN), a plug-and-play module for attention-based MIL. MSPN introduces progressive multi-scale whole-slide image analysis using only a single high-magnification input. It consists of (1) grid-based remapping that aggregates high-magnification features to derive spatially-aware coarse feature maps, and (2) the Coarse Guidance Network (CGN) that learns coarse contexts. We benchmark MSPN as an add-on module to 4 attention-based frameworks on 5 clinically relevant tasks with 2 foundation models, and a pre-trained MIL framework. Our results demonstrate that MSPN consistently improves MIL across the compared configurations and tasks, while being lightweight and easy-to-use.
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