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Semi-Supervised Virtual Staining via Morphology Preservation and Histopathological Realism Constraints

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Virtual staining aims to computationally generate target-stained histopathological images while reducing the cost and time associated with conventional staining procedures. However, existing methods rely predominantly on strictly paired and accurately registered training data, which are difficult and expensive to obtain in routine practice. To reduce this dependence, we propose a stable semi-supervised virtual staining framework that jointly exploits both limited paired data and abundant unpaired source images. Directly incorporating unpaired images is challenging because their generated resul

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