Department of Computer Science, ETH Zurich, Universitaetstrasse 6, CH-8092 Zurich, Switzerland.
Comput Med Imaging Graph. 2011 Oct-Dec;35(7-8):515-30. doi: 10.1016/j.compmedimag.2011.02.006. Epub 2011 Apr 9.
The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.
组织学评估已成为癌症检测和治疗的关键挑战。从组织微阵列数据到基因表达、蛋白质组学或代谢组学数据等大量不同的数据源为患者的健康状况提供了详细的概述。医生需要评估这些信息源，并依赖于数据驱动的自动分析工具。分类、分组和分割异构数据源的方法，以及对噪声相关性的回归和生存概率的估计，在病理诊断系统的各个处理阶段都进入了处理工作流程。本文报告了计算病理学工作流程的设计和有效性的最新进展，并讨论了医学信息学和诊断机器学习这一新兴领域的未来研究方向。
