一种用于虚拟组织染色和数字病理中幻觉检测的鲁棒可扩展框架
A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology
AI总结：
针对虚拟组织染色图像存在幻觉伪影、人工质量评估主观性强的问题，提出自主质量与幻觉评估方法AQuA，无需真值即可实现高准确率检测，与病理学家评估一致性高，适配多类组织图像，为数字病理相关任务提供自主质量保证。
摘要：
人体组织的组织病理学染色对疾病诊断至关重要。近期基于人工智能（AI）的虚拟组织染色技术取得进展，缓解了传统组织化学染色流程中部分成本高昂且繁琐的步骤，可实现多重染色并保留组织。然而，这些虚拟染色组织图像中潜在的幻觉和伪影引发了担忧，尤其是在这些方法的临床应用方面。专家对组织学图像的质量评估可能具有主观性。本文提出一种面向虚拟组织染色和数字病理的自主质量与幻觉评估方法AQuA（自主质量评估器）。在无需获取组织化学染色真值的情况下，AQuA检测合格与不合格虚拟染色组织图像的准确率可达99.8%，与委员会认证病理学家的人工评估一致性达98.5%，包括识别出可能误导诊断人员的外观逼真的图像。我们验证了AQuA在各类虚拟染色和组织化学染色的人体组织图像中的广泛适用性。该框架提升了虚拟组织染色的可靠性，为数字病理和计算成像中的图像生成与变换任务提供了自主质量保证。
英文摘要：
Histopathological staining of human tissue is essential for disease diagnosis. Recent advances in virtual tissue staining technologies using artificial intelligence (AI) alleviate some of the costly and tedious steps involved in traditional histochemical staining processes, permitting multiplexed staining and tissue preservation. However, potential hallucinations and artifacts in these virtually stained tissue images pose concerns, especially for the clinical uses of these approaches. Quality assessment of histology images by experts can be subjective. Here, we present an autonomous quality and hallucination assessment method, AQuA, for virtual tissue staining and digital pathology. AQuA autonomously achieves 99.8% accuracy when detecting acceptable and unacceptable virtually stained tissue images without access to histochemically stained ground truth, and presents an agreement of 98.5% with the manual assessments made by board-certified pathologists, including identifying realistic-looking images that could mislead diagnosticians. We demonstrate the wide adaptability of AQuA across various virtually and histochemically stained human tissue images. This framework enhances the reliability of virtual tissue staining and provides autonomous quality assurance for image generation and transformation tasks in digital pathology and computational imaging.
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