PATHS：用于高效全切片图像分析的分层Transformer
PATHS: A Hierarchical Transformer for Efficient Whole Slide Image Analysis
AI总结：
针对全切片图像分析中补丁噪声多、计算成本高的问题，提出分层选择Transformer模型PATHS，通过跨放大倍率递归筛选相关补丁，在TCGA五个数据集上以少样本处理实现更优切片级预测性能。
摘要：
近年来，全切片图像（WSIs）的计算分析取得了显著研究进展，应用涵盖生存或癌症亚型预测等重要诊断和预后任务。许多最先进的模型将可能高达150,000×150,000像素的整个切片作为大量补丁的集合进行处理，其规模需要计算成本低廉的特征聚合方法。然而，这些补丁中很大一部分无信息，例如仅包含健康或脂肪组织的补丁，为集合增加了显著噪声和规模。我们提出病理学分层选择Transformer（PATHS），一种用于计算病理学中切片级任务的新型自上而下分层弱监督表示学习方法。PATHS受人类病理学家跨放大倍率检查切片的方式启发，在每个放大倍率水平递归过滤补丁至与诊断相关的小子集。我们的方法克服了处理整个切片的复杂性，实现二次自注意力，并提供简单可解释的区域重要性度量。我们将PATHS应用于癌症基因组图谱（TCGA）的五个数据集，尽管仅处理切片的小部分，仍在切片级预测任务上取得优于先前方法的性能。
英文摘要：
Computational analysis of whole slide images (WSIs) has seen significant research progress in recent years, with applications ranging across important diagnostic and prognostic tasks such as survival or cancer subtype prediction. Many state-of-the-art models process the entire slide - which may be as large as $150,000 \times 150,000$ pixels - as a bag of many patches, the size of which necessitates computationally cheap feature aggregation methods. However, a large proportion of these patches are uninformative, such as those containing only healthy or adipose tissue, adding significant noise and size to the bag. We propose Pathology Transformer with Hierarchical Selection (PATHS), a novel top-down method for hierarchical weakly supervised representation learning on slide-level tasks in computational pathology. PATHS is inspired by the cross-magnification manner in which a human pathologist examines a slide, recursively filtering patches at each magnification level to a small subset relevant to the diagnosis. Our method overcomes the complications of processing the entire slide, enabling quadratic self-attention and providing a simple interpretable measure of region importance. We apply PATHS to five datasets of The Cancer Genome Atlas (TCGA), and achieve superior performance on slide-level prediction tasks when compared to previous methods, despite processing only a small proportion of the slide.
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