Accurate focus quality assessment (FQA) in fluorescence microscopy is challenging due to stain-dependent optical variations that induce heterogeneous focus behavior across images. Existing methods, however, treat focus quality as a stain-agnostic problem, assuming a shared global ordering. We formulate stain-aware FQA for fluorescence microscopy, showing that focus-rank relationships vary substantially across stains due to stain-dependent imaging characteristics and invalidate this assumption. To support this formulation, we introduce FluoMix, the first dataset for stain-aware FQA spanning multiple tissues, fluorescent stains, and focus levels. We further propose FluoCLIP, a two-stage vision-language framework that grounds stain semantics and enables stain-conditioned ordinal reasoning for focus prediction, effectively decoupling stain representation from ordinal structure. By explicitly modeling stain-dependent focus behavior, FluoCLIP consistently outperforms both conventional FQA methods and recent vision-language baselines, demonstrating strong generalization across diverse fluorescence microscopy conditions. Code and dataset are publicly available at https://fluoclip.github.io/.
Cite
@article{arxiv.2602.23791,
title = {FluoCLIP: Stain-Aware Focus Quality Assessment in Fluorescence Microscopy},
author = {Hyejin Park and Jiwon Yoon and Sumin Park and Suree Kim and Sinae Jang and Eunsoo Lee and Dongmin Kang and Dongbo Min},
journal= {arXiv preprint arXiv:2602.23791},
year = {2026}
}
Comments
Accepted at CVPR 2026, Project Page: https://fluoclip.github.io