HiNorm: Hierarchical Normality Learning for Whole-Slide Image Anomaly Detection

Unsupervised anomaly detection (UAD) in whole-slide images (WSIs) provides a scalable approach to detecting pathological abnormalities by learning solely from normal WSIs, without requiring abnormal samples for training or lesion-level annotations. Existing methods, however, typically learn normality from isolated patches and derive slide-level predictions by aggregating patch anomaly scores. This patch-centric paradigm overlooks the spatial organization of tissue, even though pathological assessment depends on both local morphology and global architecture, and may therefore yield unreliable WSI-level decisions. We propose HiNorm, a hierarchical normality learning framework trained exclusively on normal WSIs. HiNorm employs dual-branch reverse distillation, with frozen patch-level and WSI-level pathology foundation models providing complementary references for local tissue morphology and whole-slide organization. Its key component, Asymmetric Cross-Level Reconstruction, uses global slide context to guide patch-level reconstruction while allowing fine-grained patch evidence to inform WSI-level reconstruction. This asymmetric interaction integrates complementary information without conflating the distinct pathological roles of the two representation levels. At inference, patch-level reconstruction discrepancies are mapped spatially for anomaly localization, whereas the WSI-level discrepancy directly measures slide-level abnormality, eliminating heuristic aggregation of patch scores. Across three public WSI benchmarks, HiNorm achieves state-of-the-art performance in both anomaly localization and WSI-level detection, while substantially reducing the WSI–patch performance gap.