Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20x magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

翻译：高分辨率全切片图像（WSI）中多尺度信息的分析是数字病理学中的一项重大挑战。多实例学习（MIL）是通过对对象包（即一组较小的图像块）进行分类来处理高分辨率图像的常见解决方案。然而，此类处理通常在WSI的单尺度（如20倍放大）下进行，而忽略了人类病理学家诊断中关键的跨尺度信息。在本研究中，我们提出了一种新颖的跨尺度MIL算法，将跨尺度关系显式聚合到单个MIL网络中，用于病理图像诊断。本文的贡献有三方面：（1）提出了一种新颖的跨尺度MIL（CS-MIL）算法，该算法整合了多尺度信息及跨尺度关系；（2）创建并发布了一个具有尺度特异性形态特征的小型数据集，用于检验和可视化差异性跨尺度注意力机制；（3）凭借我们简单的跨尺度MIL策略，在内部和公开数据集上均展现出优越性能。官方实现已公开于https://github.com/hrlblab/CS-MIL。