Authors
Jiaxin Zhuang, Yao DU, Xiaoyu Zheng, Linshan Wu, Chao He, Lin Luo, Hao Chen
Published in
IEEE transactions on medical imaging. Volume PP. Jul 03, 2026. Epub Jul 03, 2026.
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.
PMID:
42397993
Bibliographic data and abstract were imported from PubMed on 04 Jul 2026.
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