Exploiting Supervision Information in Weakly Paired Images for IHC Virtual Staining

Immunohistochemical (IHC) staining plays a pivotal role in the evaluation of numerous diseases. However, the standard IHC staining process involves a series of time-consuming and labor-intensive steps, which severely hinders its application in histopathology. With the rapid advancement of deep learning techniques, virtual staining has promising potential to address this issue. But it has long been challenging to determine how to effectively provide supervision information for networks by utilizing consecutive tissue slices. To this end, we propose a weakly supervised pathological consistency constraint acting on multiple layers of GAN. Due to variations of receptive fields in different layers of the network, weakly paired consecutive slices have different degrees of alignment. Thus we allocate adaptive weights to different layers in order to dynamically adjust the supervision strengths of the pathological consistency constraint. Additionally, as an effective deep generative model, GAN can generate high-fidelity images, but it suffers from the issue of discriminator failure. To tackle this issue, a discriminator contrastive regularization method is proposed. It compels the discriminator to contrast the differences between generated images and real images from consecutive layers, thereby enhancing its capability to distinguish virtual images. The experimental results demonstrate that our method generates IHC images from H&E images robustly and identifies cancer regions accurately. Compared to previous methods, our method achieves superior results.