Semi-Supervised Virtual Staining via Morphology Preservation and Histopathological Realism Constraints

arXiv:2609.00984 · cs.CV, cs.AI · Submitted 2026-09-01 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-01

Updated: 2026-09-01

Terminology

Consistency — 9× in this paper · explained in 3 episodes
Alignment — 7× in this paper · explained in 1 episode
Robustness — 7× in this paper · explained in 5 episodes
Semi-supervised Learning — 6× in this paper · explained in 4 episodes
Foundation Model — 5× in this paper · explained in 3 episodes
Ablation — 3× in this paper · explained in 1 episode
Contrastive Learning — 2× in this paper · explained in 5 episodes
Distribution Matching — 2× in this paper · explained in 2 episodes
Structural Constraints — 2× in this paper · explained in 2 episodes
Trace — 2× in this paper · explained in 2 episodes
Ablation study — 1× in this paper · explained in 1 episode
Adaptation — 1× in this paper · explained in 1 episode
Data Augmentation — 1× in this paper · explained in 7 episodes
Diffusion Model — 1× in this paper · explained in 8 episodes
Diffusion Models — 1× in this paper · explained in 6 episodes
Domain Adaptation — 1× in this paper · explained in 5 episodes
Fidelity — 1× in this paper · explained in 2 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
Pseudo-labeling — 1× in this paper · explained in 3 episodes
Regularization — 1× in this paper · explained in 3 episodes
Representation Learning — 1× in this paper · explained in 2 episodes
Transfer Learning — 1× in this paper · explained in 10 episodes
Transformer — 1× in this paper · explained in 2 episodes

Sources

UNIStainNet: Foundation-Model-Guided Virtual Staining of H&E to IHC

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