Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Department of Electrical Engineering, University of South Florida, Tampa, Florida.
Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Department of Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; University of South Florida, Morsani College of Medicine, Tampa, Florida.
Lab Invest. 2023 Nov;103(11):100255. doi: 10.1016/j.labinv.2023.100255. Epub 2023 Sep 26.
Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enabling computational pathology. By leveraging artificial intelligence (AI) and machine learning (ML), computational pathology has emerged as a promising field in recent years. Recently, task-specific AI/ML (eg, convolutional neural networks) has risen to the forefront, achieving above-human performance in many image-processing and computer vision tasks. The performance of task-specific AI/ML models depends on the availability of many annotated training datasets, which presents a rate-limiting factor for AI/ML development in pathology. Task-specific AI/ML models cannot benefit from multimodal data and lack generalization, eg, the AI models often struggle to generalize to new datasets or unseen variations in image acquisition, staining techniques, or tissue types. The 2020s are witnessing the rise of foundation models and generative AI. A foundation model is a large AI model trained using sizable data, which is later adapted (or fine-tuned) to perform different tasks using a modest amount of task-specific annotated data. These AI models provide in-context learning, can self-correct mistakes, and promptly adjust to user feedback. In this review, we provide a brief overview of recent advances in computational pathology enabled by task-specific AI, their challenges and limitations, and then introduce various foundation models. We propose to create a pathology-specific generative AI based on multimodal foundation models and present its potentially transformative role in digital pathology. We describe different use cases, delineating how it could serve as an expert companion of pathologists and help them efficiently and objectively perform routine laboratory tasks, including quantifying image analysis, generating pathology reports, diagnosis, and prognosis. We also outline the potential role that foundation models and generative AI can play in standardizing the pathology laboratory workflow, education, and training.
数字病理学将传统的显微镜下组织分析方法转变为计算机视觉工作流程。全切片成像允许病理学家在计算机显示器上查看和分析显微镜图像，从而实现计算病理学。通过利用人工智能 (AI) 和机器学习 (ML)，计算病理学近年来已成为一个有前途的领域。最近，特定于任务的 AI/ML（例如卷积神经网络）已崭露头角，在许多图像处理和计算机视觉任务中实现了超越人类的性能。特定于任务的 AI/ML 模型的性能取决于可用的大量标注训练数据集，这是 AI/ML 在病理学中发展的一个限制因素。特定于任务的 AI/ML 模型无法从多模态数据中受益，并且缺乏泛化能力，例如，AI 模型通常难以泛化到新数据集或图像采集、染色技术或组织类型的新变化。21 世纪 20 年代见证了基础模型和生成式 AI 的兴起。基础模型是使用大量数据训练的大型 AI 模型，然后使用少量特定于任务的标注数据对其进行调整（或微调）以执行不同的任务。这些 AI 模型提供上下文学习，可以自我纠正错误，并及时调整以响应用户反馈。在这篇综述中，我们简要介绍了计算病理学领域最近通过特定于任务的 AI 取得的进展，以及它们面临的挑战和局限性，然后介绍了各种基础模型。我们建议基于多模态基础模型创建特定于病理学的生成式 AI，并提出其在数字病理学中的潜在变革作用。我们描述了不同的用例，阐明了它如何作为病理学家的专家伴侣，帮助他们高效、客观地完成常规实验室任务，包括量化图像分析、生成病理报告、诊断和预后。我们还概述了基础模型和生成式 AI 在标准化病理实验室工作流程、教育和培训方面可以发挥的潜在作用。
