Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-world datasets for cross-domain and cross-discipline prediction and classification tasks. DL architectures excel in computer vision tasks, and in particular image processing and interpretation. This has prompted a wave of disruptingly innovative applications in medical imaging, where DL strategies have the potential to vastly outperform human experts. This is particularly relevant in the context of histopathology, where whole slide imaging (WSI) of stained tissue in conjuction with DL algorithms for their interpretation, selection and cancer staging are beginning to play an ever increasing role in supporting human operators in visual assessments. This has the potential to reduce everyday workload as well as to increase precision and reproducibility across observers, centers, staining techniques and even pathologies. In this paper we introduce the most common DL architectures used in image analysis, with a focus on histopathological image analysis in general and in breast histology in particular. We briefly review how, state-of-art DL architectures compare to human performance on across a number of critical tasks such as mitotic count, tubules analysis and nuclear pleomorphism analysis. Also, the development of DL algorithms specialized to pathology images have been enormously fueled by a number of world-wide challenges based on large, multicentric image databases which are now publicly available. In turn, this has allowed most recent efforts to shift more and more towards semi-supervised learning methods, which provide greater flexibility and applicability. We also review all major repositories of manually labelled pathology images in breast cancer and provide an in-depth discussion of the challenges specific to training DL architectures to interpret WSI data, as well as a review of the state-of-the-art methods for interpretation of images generated from immunohistochemical analysis of breast lesions. We finally discuss the future challenges and opportunities which the adoption of DL paradigms is most likely to pose in the field of pathology for breast cancer detection, diagnosis, staging and prognosis. This review is intended as a comprehensive stepping stone into the field of modern computational pathology for a transdisciplinary readership across technical and medical disciplines.

深度学习 (DL) 算法是一组利用大型和/或复杂现实世界数据集进行跨领域和跨学科预测和分类任务的技术。深度学习架构擅长计算机视觉任务，尤其是图像处理和解释。这引发了医学成像领域的颠覆性创新应用浪潮，其中深度学习策略有可能大大超越人类专家。这在组织病理学的背景下尤其重要，其中染色组织的全玻片成像 (WSI) 与用于其解释、选择和癌症分期的 DL 算法相结合，开始在支持人类操作员进行视觉评估方面发挥越来越大的作用。这有可能减少日常工作量，并提高观察者、中心、染色技术甚至病理学的精确度和可重复性。在本文中，我们介绍了图像分析中最常见的深度学习架构，重点关注一般的组织病理学图像分析，特别是乳腺组织学。我们简要回顾了最先进的深度学习架构如何与人类在有丝分裂计数、小管分析和核多态性分析等许多关键任务上的表现进行比较。此外，基于现在公开可用的大型多中心图像数据库的全球范围内的许多挑战极大地推动了专门针对病理图像的 DL 算法的发展。反过来，这使得最近的努力越来越多地转向半监督学习方法，这提供了更大的灵活性和适用性。我们还回顾了手动标记的乳腺癌病理图像的所有主要存储库，并深入讨论了训练 DL 架构以解释 WSI 数据所面临的挑战，并回顾了最先进的方法解释从乳腺病变的免疫组织化学分析产生的图像。最后，我们讨论了采用深度学习范式最有可能在乳腺癌检测、诊断、分期和预后的病理学领域带来的未来挑战和机遇。