Accurate Classification of Pathological Whole-Slide Images for Out-of-Distribution Generalization

Abstract

WSI-based classification often suffers from out-of-distribution (OOD) generalization due to the distribution mismatch between training on mixed patches from multiple WSIs and testing on individual WSIs with varying tissue compositions. This prior shift impairs model generalization and degrades performance. To address this issue, we propose two distribution alignment strategies: intra-WSI rearrange and inter-WSI rearrange, which, respectively, regulate patch distribution within individual WSIs and across different WSIs. These strategies are embedded into a transformer-based multi-instance learning (MIL) framework enabling more accurate and robust classification. Our method achieves excellent AUC scores of 0.959 and 0.963 on the CAMELYON16 and TCGA-NSCLC datasets, respectively. Moreover, it reaches an average AUC of 0.974 in 5-fold cross-validation on a private CRC dataset, matching the performance of patch-based approaches. Ablation studies further validate the effectiveness of our proposed strategies in mitigating the OOD challenge in WSI classification. Overall, these strategies enhance the robustness and accuracy of WSI-based models in handling OOD challenges.