Autonomous Quality and Hallucination Assessment for Virtual Tissue Staining and Digital Pathology

• URL: http://arxiv.org/abs/2404.18458v1
• Date: Mon, 29 Apr 2024 06:32:28 GMT
• Title: Autonomous Quality and Hallucination Assessment for Virtual Tissue Staining and Digital Pathology
• Authors: Luzhe Huang, Yuzhu Li, Nir Pillar, Tal Keidar Haran, William Dean Wallace, Aydogan Ozcan,
• Abstract summary: We present an autonomous quality and hallucination assessment method (termed AQuA) for virtual tissue staining. AQuA achieves 99.8% accuracy when detecting acceptable and unacceptable virtually stained tissue images.
• Score: 0.11728348229595655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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