Graph Domain Adaptation With Dual-Branch Encoder and Two-Level Alignment for Whole Slide Image-Based Survival Prediction

In recent years, whole slide image (WSI)-based survival analysis has attracted much attention. In practice, WSIs usually come from different hospitals (or domains) and may have significant differences. These differences generally result in large gaps in distribution between different WSI domains and thus, the survival analysis models trained on one domain may fail to transfer to another. To address this issue, we propose a Dual-branch Encoder and Two-level Alignment (DETA) framework to explore both feature and category-level alignment between different WSI domains. Specifically, we first formulate the concerned problem as graph domain adaptation (GDA) using the graph representation of WSIs. Then, we construct a dual-branch graph encoder, including the message passing (MP) and the shortest path (SP) branches, to explicitly and implicitly extract semantic information from the graph-represented WSIs. To realize GDA, we propose a two-level alignment approach: at the category level, we develop a coupling technique by virtue of the dual-branch structure, leading to reduced divergence between the category distributions of the two domains; at the feature level, we introduce an adversarial perturbation strategy to better augment source domain feature, resulting in improved alignment in feature distribution. Extensive experiments have demonstrated the effectiveness of our proposed DETA framework in WSI-based survival analysis under the domain shift scenario.