用于数字病理学中多实例学习的结构化状态空间模型
Structured State Space Models for Multiple Instance Learning in Digital Pathology
AI总结：
本文提出将结构化状态空间模型作为多实例学习器应用于数字病理学，通过在转移检测、癌症亚型分类等任务上的实验，证明其在处理长序列时与现有最先进方法具有竞争力。
摘要：
多实例学习是组织病理学数据的一种理想分析模式，其中巨大的全切片图像通常带有单一全局标签。在这种情况下，全切片图像被建模为待聚合和分类的组织切片集合。执行此分类的常见模型包括循环神经网络和transformers。尽管使用强大的压缩算法（如深度预训练神经网络）来降低每个切片的维度，但由全切片图像产生的序列仍然过长，通常包含数万个切片。结构化状态空间模型是序列建模的新兴替代方案，专为长序列的高效建模而设计。这些模型将输入序列最优投影到压缩整个序列的记忆单元中。在本文中，我们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中的各种问题。通过在转移检测、癌症亚型分类、突变分类和多任务学习中的实验，我们证明了这类新模型与现有最先进方法相比具有竞争力。我们的代码可在 https://github.com/MICS-Lab/s4_digital_pathology 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
