提出一种检测虚拟染色中幻觉的新方法，提升病理分析可靠性。
Hallucination Detection in Virtually-Stained Histology: A Latent Space Baseline
- 利用生成器潜空间预判虚拟染色中的幻觉
- 在多种任务中均表现有效且鲁棒
- 揭示当前评估体系缺陷，推动建立幻觉检测基准
组织病理学染色仍是生物医学研究与临床诊疗的核心。虚拟染色（VS）虽具降低成本、优化流程的潜力，但其产生的幻觉严重威胁临床可靠性。本文首次形式化了虚拟染色中幻觉检测问题，并提出一种可扩展的后处理方法：神经幻觉前兆（NHP），通过分析生成器的潜空间实现幻觉的提前预警。在多种不同的虚拟染色任务上进行的大量实验表明，NHP具有良好的有效性与鲁棒性。尤为重要的是，我们发现幻觉较少的模型并不一定具备更好的可检测性，暴露出现有虚拟染色评估体系的缺口，凸显建立幻觉检测基准的紧迫性。
原文摘要 · Abstract (English)
Histopathologic analysis of stained tissue remains central to biomedical research and clinical care. Virtual staining (VS) offers a promising alternative, with potential to reduce costs and streamline workflows, yet hallucinations pose serious risks to clinical reliability. Here, we formalize the problem of hallucination detection in VS and propose a scalable post-hoc method: Neural Hallucination Precursor (NHP), which leverages the generator's latent space to preemptively flag hallucinations. Extensive experiments across diverse VS tasks show NHP is both effective and robust. Critically, we also find that models with fewer hallucinations do not necessarily offer better detectability, exposing a gap in current VS evaluation and underscoring the need for hallucination detection benchmarks.
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