面向数字病理学的多示例学习：最新进展、局限性与未来潜力综述
Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential
AI总结：
本文综述深度多示例学习在数字病理全切片图像分析中的主流方法、近三年进展、挑战与未来潜力，以解决缺乏细粒度标注的训练难题。
摘要：
数字全切片图像包含海量信息，这为自动化图像分析工具的开发提供了强大动力。尤其是深度神经网络，在数字病理学领域的各类任务中展现出巨大潜力。然而，一个局限在于：典型的深度学习算法除了需要大量图像数据外，还需要（人工）标注，才能实现有效训练。多示例学习（Multiple Instance Learning）为在缺乏完整标注数据的场景下训练深度神经网络提供了一种强有力的工具。这些方法在该领域尤其有效，原因在于整张全切片图像的标签通常会被常规采集，而图像块、区域或像素级标签却并非如此。这一潜力已经催生了大量出版物，其中大多数发表于最近三年。除了数据的可获得性以及来自医学视角的强烈动机之外，高性能图形处理器的可用性也成为该领域的加速器。在本文中，我们概述了被广泛且有效使用的深度多示例学习方法概念、最新进展，并批判性地讨论了尚存挑战与未来潜力。
英文摘要：
Digital whole slides images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural networks show high potential with respect to various tasks in the field of digital pathology. However, a limitation is given by the fact that typical deep learning algorithms require (manual) annotations in addition to the large amounts of image data, to enable effective training. Multiple instance learning exhibits a powerful tool for learning deep neural networks in a scenario without fully annotated data. These methods are particularly effective in this domain, due to the fact that labels for a complete whole slide image are often captured routinely, whereas labels for patches, regions or pixels are not. This potential already resulted in a considerable number of publications, with the majority published in the last three years. Besides the availability of data and a high motivation from the medical perspective, the availability of powerful graphics processing units exhibits an accelerator in this field. In this paper, we provide an overview of widely and effectively used concepts of used deep multiple instance learning approaches, recent advances and also critically discuss remaining challenges and future potential.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
