IT与预测性诊断

K. Kayser
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引用次数: 0

摘要

只提供摘要形式。信息减少了接受者以某种最佳方式作出反应的不确定性。它是一种统计性质,或者是统计总体中的一个事件,可以通过不同的熵算法来计算。在图像分析中,人们应该区分图像内容信息(ICI), ICI是可以从图像本身评估的信息,而外部知识不包括在图像本身中,并且通常必须以正确的方式解释ICI。诊断可以定义为对ICI的外部信息的映射。具体的算法如何做到这一点将重点讨论预测诊断。预测性诊断分析细胞内和细胞外通路,包括基因异常。它是开发和应用药物策略以指导个体化癌症治疗的工具。详细描述了熵测量的意义,包括结构熵(MST)熵和图像标准化,并在个别情况下进行了演示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IT and predictive diagnosis
Summary form only given. Information diminishes the uncertainty of a receiver to react in a certain optimum manner. It is a statistical property, or an event in a statistical population, and can be calculated by different entropy algorithms.. In image analysis one should distinguish between image content information (ICI), which is information that can be evaluated from the image itself and external knowledge which is not included in the image itself and which is commonly mandatory to interpret ICI in a correct manner. Diagnosis can then be defined as a mapping of external information on ICI. The specific algorithms how to do this will be discussed with focus on predictive diagnosis. Predictive diagnosis analyses intra- and extra-cellular pathways including gene abnormalities. It is the tool to develop and apply drug strategies in order to steer individualized cancer therapy. The significance of entropy measurements including structural (MST) entropy and image standardization is described in detail and demonstrated on individual cases.
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