MUST: Multi-style virtual staining with incomplete pairs.

Abstract

Multi-style virtual staining transforms histological images into multiple staining modalities, offering significant clinical value at reduced cost and time. However, a critical challenge impeding clinical adoption is incompletely paired training data-an inevitable consequence of tissue degradation and processing artifacts during sequential staining. Current methods assume perfectly paired datasets, severely limiting their clinical utility. We address this problem by introducing MUST (MUlti-style virtual STaining), which reformulates virtual staining as progressive cross-modality refinement under incomplete supervision. Our approach comprises two synergistic components: (1) Collaborative Denoising (CoDe) that uses cross-modality cross attention to condition a latent diffusion model, enabling effective information exchange across modalities with incomplete supervision, and (2) Semantic Preservation (SP) that further maintains cross-modal consistency through contrastive learning while generating reliable pseudo-supervision from confident model predictions in samples without ground truth. Extensive experiments across three histopathology datasets demonstrate that MUST significantly outperforms state-of-the-art methods, effectively mining cross-modality correlations while generating high-confidence pseudo-supervision from incomplete data. Code and trained models will be publicly released upon publication at https://github.com/JiaxinZhuang/MUST.