Revisiting Reconstruction-Based Anomaly Detection for Whole-Slide Image

Whole Slide Images (WSIs) have been widely used in computational pathology (CPath) for various tasks. However, obtaining high-quality annotations remains a major bottleneck. Task-aware unsupervised anomaly detection models offer a promising alternative, as they are trained solely on task-specific normal data and can be adapted to clinically defined objectives, such as cancer detection, depending on the problem formulation. Despite this potential, anomaly detection models have not been thoroughly explored in the context of WSIs. Existing approaches often directly adopt techniques from other domains, leading to suboptimal performance due to domain discrepancies and the unique characteristics of WSIs. Given that feature reconstruction-based methods have become popular in anomaly detection research, this study first analyzes the designs of such models in the context of conditional reconstruction, revealing the potential directions to adapt and further improve these models. Based on our analysis, we revisit and refine them to better accommodate the distinct properties of WSIs. Moreover, we propose an Explicit Conditional Reconstruction framework, termed as ECR4AD, which can significantly enhance model performance. Our method is comprehensively evaluated on four datasets covering breast and prostate cancer metastasis detection, as well as Gleason grading of prostate cancer, all conducted at the tile level on images extracted from WSIs. The experimental results show that ECR4AD consistently achieves substantial improvements in AUROC across all datasets, demonstrating its effectiveness for tile-level task-aware unsupervised anomaly detection in CPath. The code can be found at https://github.com/uobinxiao/wsi_anomaly_detection.git.