通过潜在流形压缩的组织病理图像标准化
Histopathology Image Normalization via Latent Manifold Compaction
AI总结：
本文提出LMC方法，通过压缩染色诱导的潜在流形实现组织病理图像标准化，有效减少批次效应，提升跨批次任务的泛化能力。
摘要：
来自组织病理染色协议、扫描仪和获取流程的技术变异引起的批次效应，一直是计算病理学中的持续挑战，阻碍了跨批次泛化并限制了模型在临床站点的可靠部署。在本文中，我们引入了潜在流形压缩（LMC），一种无监督的表示学习框架，通过从单一数据集学习批次不变的嵌入来执行图像和谐化，通过显式压缩染色诱导的潜在流形来实现。这使LMC能够泛化到训练期间未见过的目标域数据。在三个具有挑战性的公开和内部基准上评估，LMC显著减少了多个数据集中的批次诱导分离，并在下游跨批次分类和检测任务中一致优于最先进的标准化方法，实现了更优的泛化。
英文摘要：
Batch effects arising from technical variations in histopathology staining protocols, scanners, and acquisition pipelines pose a persistent challenge for computational pathology, hindering cross-batch generalization and limiting reliable deployment of models across clinical sites. In this work, we introduce Latent Manifold Compaction (LMC), an unsupervised representation learning framework that performs image harmonization by learning batch-invariant embeddings from a single source dataset through explicit compaction of stain-induced latent manifolds. This allows LMC to generalize to target domain data unseen during training. Evaluated on three challenging public and in-house benchmarks, LMC substantially reduces batch-induced separations across multiple datasets and consistently outperforms state-of-the-art normalization methods in downstream cross-batch classification and detection tasks, enabling superior generalization.
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