Weakly Supervised Virtual Immunohistochemistry Staining via Schrödinger Bridge Method

Immunohistochemistry (IHC) staining provides precise localization and qualitative analysis for tumor diagnosis, while its application is often constrained by complexity and high costs. Recently, many researchers have employed virtual staining based on deep learning to translate hematoxylin and eosin (H&E) images into IHC images, presenting a more efficient and cost-effective alternative. However, these methods usually rely on Generative Adversarial Networks (GANs), which are prone to various issues such as mode collapse, thereby limiting the effectiveness. To address this issue, we propose a weakly supervised virtual staining method based on the Schrödinger bridge called StainSB. This method establishes an optimal random process between the source and target distributions, effectively translating H&E images into IHC images. Specifically, we design a regional color state loss to model the pathological similarity between the generated and real IHC images, thereby incorporating pathological information into the generation process. Furthermore, the proposed aggregation strategy enables the generated images to achieve a balance between image quality and pathological consistency. Extensive experiments on two public benchmark datasets show that the proposed StainSB method achieves state-of-the-art performance across multiple metrics.