A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908408197677056 |
|---|---|
| author | Huang, Luzhe Li, Yuzhu Pillar, Nir Haran, Tal Keidar Wallace, William Dean Ozcan, Aydogan |
| author_facet | Huang, Luzhe Li, Yuzhu Pillar, Nir Haran, Tal Keidar Wallace, William Dean Ozcan, Aydogan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18458 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology Huang, Luzhe Li, Yuzhu Pillar, Nir Haran, Tal Keidar Wallace, William Dean Ozcan, Aydogan Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Medical Physics 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. |
| title | A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Medical Physics |
| url | https://arxiv.org/abs/2404.18458 |