Multiple instance learning is an ideal mode of analysis for histopathology data, where vast whole slide images are typically annotated with a single global label. In such cases, a whole slide image is modelled as a collection of tissue patches to be aggregated and classified. Common models for performing this classification include recurrent neural networks and transformers. Although powerful compression algorithms, such as deep pre-trained neural networks, are used to reduce the dimensionality of each patch, the sequences arising from whole slide images remain excessively long, routinely containing tens of thousands of patches. Structured state space models are an emerging alternative for sequence modelling, specifically designed for the efficient modelling of long sequences. These models invoke an optimal projection of an input sequence into memory units that compress the entire sequence. In this paper, we propose the use of state space models as a multiple instance learner to a variety of problems in digital pathology. Across experiments in metastasis detection, cancer subtyping, mutation classification, and multitask learning, we demonstrate the competitiveness of this new class of models with existing state of the art approaches. Our code is available at https://github.com/MICS-Lab/s4_digital_pathology.

翻译：多实例学习是分析组织病理学数据的理想模式，其中全切片图像通常仅附有一个全局标签。在这种情形下，全切片图像被建模为待聚合和分类的组织补丁集合。常见的分类模型包括循环神经网络和Transformer。尽管采用深度预训练神经网络等强大的压缩算法来降低每个补丁的维度，但全切片图像生成的序列仍然过长，通常包含数万个补丁。结构化状态空间模型作为序列建模的新兴替代方案，专为高效处理长序列而设计。这类模型将输入序列最优映射到记忆单元中，从而压缩整个序列。本文提出将状态空间模型作为多实例学习器，应用于数字病理学的多种问题。在转移灶检测、癌症亚型分类、突变分类以及多任务学习等实验中，我们证明了这类新模型相较于现有最先进方法的竞争力。我们的代码已开源在 https://github.com/MICS-Lab/s4_digital_pathology。