通过压缩染色特征空间，实现病理图像跨批次统一
Histopathology Image Normalization via Latent Manifold Compaction
- 从单源数据学习批量无关表征，显式压缩染色引起的潜在流形
- 在多个公开和自建数据集上显著减少批次差异，提升分类与检测性能
- 无需标注即可跨批次泛化，适合临床多中心部署
组织病理学染色流程、扫描仪及采集路径的技术差异导致的批次效应，持续阻碍计算病理学的跨批次泛化，限制模型在不同临床场景中的可靠部署。本文提出无监督表示学习框架Latent Manifold Compaction（LMC），通过显式压缩染色诱导的潜在流形，从单一源数据集中学习批量无关的嵌入表示，使模型可泛化至训练时未见的目标域数据。在三个具有挑战性的公开及内部基准上评估，LMC显著降低了多数据集间的批次差异，并在下游跨批次分类与检测任务中持续优于现有最先进方法，实现更优的泛化能力。
原文摘要 · Abstract (English)
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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