School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China.
Front Med. 2020 Aug;14(4):470-487. doi: 10.1007/s11684-020-0782-9. Epub 2020 Jul 29.
Deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. In terms of feature extraction, DL approaches are less labor intensive compared with conventional machine learning methods. In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classification, semantic segmentation, detection, and instance segmentation) and various applications (e.g., stain normalization, cell/gland/region structure analysis). DL methods can provide consistent and accurate outcomes. DL is a promising tool to assist pathologists in clinical diagnosis.
深度学习（DL）在许多数字病理学分析任务中取得了最先进的性能。传统方法通常需要手工制作特定于领域的特征，而 DL 方法可以学习无需人工设计特征的表示。在特征提取方面，与传统的机器学习方法相比，DL 方法的劳动强度更低。在本文中，我们全面总结了组织病理学中基于深度学习的图像分析研究，包括不同的任务（例如分类、语义分割、检测和实例分割）和各种应用（例如染色归一化、细胞/腺体/区域结构分析）。DL 方法可以提供一致和准确的结果。DL 是一种很有前途的工具，可以帮助病理学家进行临床诊断。
