Histopathological staining of human tissue is essential in the diagnosis of various diseases. The recent advances in virtual tissue staining technologies using AI alleviate some of the costly and tedious steps involved in the traditional histochemical staining process, permitting multiplexed rapid staining of label-free tissue without using staining reagents, while also preserving tissue. However, potential hallucinations and artifacts in these virtually stained tissue images pose concerns, especially for the clinical utility of these approaches. Quality assessment of histology images is generally performed by human experts, which can be subjective and depends on the training level of the expert. Here, we present an autonomous quality and hallucination assessment method (termed AQuA), mainly designed for virtual tissue staining, while also being applicable to histochemical staining. AQuA achieves 99.8% accuracy when detecting acceptable and unacceptable virtually stained tissue images without access to ground truth, also presenting an agreement of 98.5% with the manual assessments made by board-certified pathologists. Besides, AQuA achieves super-human performance in identifying realistic-looking, virtually stained hallucinatory images that would normally mislead human diagnosticians by deceiving them into diagnosing patients that never existed. We further demonstrate the wide adaptability of AQuA across various virtually and histochemically stained tissue images and showcase its strong external generalization to detect unseen hallucination patterns of virtual staining network models as well as artifacts observed in the traditional histochemical staining workflow. This framework creates new opportunities to enhance the reliability of virtual staining and will provide quality assurance for various image generation and transformation tasks in digital pathology and computational imaging.

翻译：人体组织苏木精-伊红染色是多种疾病诊断的关键步骤。基于人工智能的虚拟组织染色技术近年来取得突破，该技术无需染色试剂即可实现无标记组织的多重快速染色，在省去传统组织化学染色流程中昂贵繁琐步骤的同时保留组织样本。然而，这些虚拟染色组织图像中潜在的结构幻觉与伪影问题，尤其对其临床应用价值构成挑战。组织学图像质量评估通常由人类专家完成，但该方法存在主观性，且评估结果高度依赖于专家的专业训练水平。本文提出一种自主质量与幻觉评估方法（命名为AQuA），该方法主要面向虚拟组织染色场景，同时可推广至组织化学染色领域。在无需参考真实染色图像的情况下，AQuA对虚拟染色组织图像可接受性的判定准确率达99.8%，与经过认证的病理学家人工评估结果一致性达98.5%。特别值得关注的是，AQuA在识别具有逼真外观的虚拟染色幻觉图像方面展现出超人类能力——这类图像常误导诊断人员对不存在的患者做出错误诊断。我们进一步验证了AQuA对多种虚拟染色与组织化学染色组织图像的广泛适应性，并展示了其强大的外推泛化能力：既可检测虚拟染色网络模型未见过的幻觉模式，也能识别传统组织化学染色流程中出现的伪影。该框架为提升虚拟染色可靠性提供了新路径，将为数字病理学与计算成像领域的各类图像生成与转换任务提供质量保障。