Improving Cross-Organ Generalization in Histopathology Segmentation via Evidence-Guided Vision-Language Query Decoding

Domain shift remains a major obstacle to robust histopathology image segmentation, especially when models trained on several source organs are deployed to unseen anatomical sites. This study addresses cross-organ adenocarcinoma segmentation by introducing an evidence-guided vision-language segmentation framework that incorporates pathology-relevant morphological evidence into dense mask prediction. The proposed method uses a pathology vision-language encoder to extract image and text representations, a semantic query booster to form image-aware segmentation queries, and an evidence-guided decoding module that integrates positive tumour-supporting evidence and negative misleading evidence. A feature-level style regularization branch is further used during training to improve robustness to source-domain appearance variation. Experiments were conducted on cross-organ adenocarcinoma datasets under a source-only domain generalization setting, using colorectum, stomach, and pancreas as source domains and ampullary, gallbladder, and intestine as unseen target domains. The proposed framework achieved 79.48%/88.57% IoU/Dice on seen source organs and 79.89%/88.82% on unseen target organs, with a small seen-unseen gap of 0.41 IoU points and 0.25 Dice points. These results suggest that structured pathology evidence can provide useful semantic guidance for cross-organ tumour segmentation and may reduce reliance on organ-specific visual shortcuts.