FluoCLIP: Stain-Aware Focus Quality Assessment in Fluorescence Microscopy

TL;DR

FluoCLIP introduces a stain-aware approach for focus quality assessment in fluorescence microscopy, leveraging a new dataset and a vision-language model to improve accuracy across diverse staining conditions.

Contribution

The paper presents FluoMix, a stain-aware dataset, and FluoCLIP, a novel vision-language framework that explicitly models stain-dependent focus behavior for better assessment.

Findings

FluoCLIP outperforms conventional methods in focus prediction accuracy.

The dataset spans multiple tissues, stains, and focus levels, supporting stain-aware analysis.

Explicit stain modeling improves generalization across microscopy conditions.

Abstract

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/.