PathVLG: A Vision-Language Framework for Domain Generalization in Cross-Organ Adenocarcinoma Segmentation

Domain shifts in computational pathology, caused by variations in staining, imaging devices, and tissue morphology, challenge model performance in segmentation tasks. Existing domain generalization methods, such as style transfer and feature alignment, often fail to account for organ-level morphological differences. In this paper, we propose PathVLG, a visionlanguage model designed to improve domain generalization for adenocarcinoma segmentation. PathVLG leverages a CONCHbased encoder with three key innovations: the Text-informed Content Query Reformer (TCQR), Text-driven Style Augmentor (TSA), and Style Regeneration Decoder (SRD). These components help the model adapt across domains by incorporating text embeddings, generating diverse styles, and combining source and target domain features. Experimental results show that PathVLG outperforms existing methods in cross-domain generalization.