数字全幻灯片图像包含大量信息，为开发自动图像分析工具提供了强大的动力。特别是深度神经网络在数字病理学领域的各种任务方面显示出很高的潜力。然而,典型的深度学习算法除了需要大量的图像数据外，还需要（手动）注释，进行有效的培训。多实例学习展示了在没有完全注释数据的情况下训练深度神经网络的强大工具。这些方法在数字病理学领域特别有效，由于整个幻灯片图像的标签通常是常规捕获的，而补丁的标签，regions,或像素不是。这种潜力导致了相当多的出版物，绝大多数都是在过去四年出版的。除了数字化数据的可用性和从医学角度来看的高动机，强大的图形处理单元的可用性展示了这一领域的加速器。在本文中,我们提供了广泛和有效使用的概念（深度）多实例学习方法和最新进展的概述。我们还批判性地讨论了剩余的挑战以及未来的潜力。
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 training deep neural networks in a scenario without fully annotated data. These methods are particularly effective in the domain of digital pathology, due to the fact that labels for whole slide images are often captured routinely, whereas labels for patches, regions, or pixels are not. This potential resulted in a considerable number of publications, with the vast majority published in the last four years. Besides the availability of digitized 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 (deep) multiple instance learning approaches and recent advancements. We also critically discuss remaining challenges as well as future potential.
