Abstract：
Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.
摘要：
诊断设备,方法论方法，和传统的临床病理学实践结构，栽培了几个世纪，在爆炸性的技术增长和其他方面发生了根本性的变化，例如，环境,变化的催化剂。数字成像设备和机器学习（ML）软件被引入现代实验室医学的竞争中，以减轻挑战。例如，在大数据时代，临床医生为环境和诊断信息的新兴互联做好准备。随着计算机视觉为现代世界塑造新的结构，并与临床医学交织在一起，通过检查计算病理学的轨迹和当前范围及其与临床实践的相关性来培养我们新地形的清晰度至关重要。通过对大量研究的回顾，我们发现ML从研究迁移到标准化临床框架的发展努力，同时克服了以前限制采用这些工具的障碍，例如，概括性，数据可用性，和用户友好的可访问性。开创性的验证工作促进了ML工具的临床部署，证明了有效帮助区分肿瘤亚型和级别的能力。早期分类与晚期癌症阶段，并协助质量控制和初级诊断应用。案例研究已经证明了精简的好处，为从业者提供数字化工作流程，减轻了负担。
