AdvLIF: An Adversarially and Physics-Inspired Framework for Virtual Staining and Nucleus Segmentation

Existing deep learning methods for virtual staining often produce quantitatively unreliable results, hindering their use for precise single-cell analysis. This limitation is associated with treating cross-stain reconstruction as a simple image style-transfer problem despite the substantial differences in appearance and information content between the source IHC image and the target morphological and molecular representations. In this work, we introduce AdvLIF, an adversarially regularized and physics-inspired framework for jointly generating H&E-like and complementary auxiliary modalities from IHC images and segmenting nuclei. AdvLIF incorporates reaction-diffusion- and Poisson-inspired operators for latent feature evolution and boundary-aware reconstruction, together with adversarially regularized fusion and skip-routing mechanisms. Extensive evaluations demonstrate that AdvLIF not only surpasses state-of-the-art methods in segmentation accuracy, achieving leading Dice and IoU scores of 0.775/0.636 on BCData and 0.765/0.623 on DeepLIIF, but also achieves superior quantitative fidelity, exemplified by the lowest error (0.092) on the IHC Quantification Difference metric.