FOCAL: Agent-guided tissue masking by visual reconciliation of segmentation disagreements

Tissue detection in whole-slide images (WSIs) is a critical preprocessing step for computational pathology pipelines. Classical approaches rely on thresholding and morphological operations, while deep learning can improve accuracy but depends on diverse annotated training data and can still struggle to generalize across organs and stains, particularly on challenging slides. We propose FOCAL (Foundation model and Oracle Classification for Annotation-free tissue Localization), an agent-guided tissue detection pipeline that combines candidate masks with visual reasoning to resolve segmentation disagreements without additional task-specific annotation or detector fine-tuning. FOCAL separates consensus from disputed regions, uses a vision-language agent to adjudicate tissue versus background in the original image context, and reconstructs the final mask while retaining regional decision reasons and artifact labels. We evaluate on four datasets (TWI, HEST, ACROBAT and KPMP) totaling 6,203 WSIs across diverse tissue types, stains and scanning platforms. FOCAL achieves strong overall performance and is especially robust on challenging slides with artifacts, weak staining and fragmented tissue, etc. Beyond mask prediction, we organize artifact-labelled regions into a traceable resource for targeted retrieval of challenging cases and future artifact-detection, quality-control and tissue-segmentation robustness benchmarks after human verification and necessary revision. Code, generated tissue masks, documented artifact annotations, and curated challenge sets will be released upon paper acceptance.