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.

翻译：多实例学习（MIL）在计算病理学（CPath）中广泛应用，其中多尺度特征对于捕捉精细细胞细节与宽域组织结构至关重要。然而，现有基于多尺度的MIL方法通常依赖僵化的多倍率输入或计算开销高昂的架构。随着预训练基础模型（FM）成为特征提取的主流趋势并推动轻量化模型发展，我们重新思考并探索更高效的多尺度MIL方法。本文提出多尺度金字塔网络（MSPN），这是一种即插即用的注意力机制MIL模块。MSPN仅需单张高倍率输入即可实现渐进式多尺度全切片图像分析，其包含：（1）基于网格的重映射模块，通过聚合高倍率特征生成空间感知的粗粒度特征图；（2）粗粒度引导网络（CGN），用于学习上下文粗粒度信息。我们在5项临床任务中，将MSPN作为附加模块集成至4种基于注意力的框架，并结合2种基础模型及一个预训练MIL框架进行基准测试。结果表明，MSPN在保持轻量化与易用性的同时，可在所有对比配置与任务中稳定提升MIL性能。