To overcome the shortcomings of radiomics methods, we developed a more advanced method, called deep learning-based radiomics (DLR). DLR obtains radiomics features by normalizing the information from a deep neural network designed for image segmentation. The main assumption of DLR is that once the image has been segmented accurately by the deep neural network, all the information about the segmented region will have already been installed in the network. Unlike current radiomics methods, in DLR, the high-throughput image features are directly extracted from the deep neural network. Because DLR does not involve extra feature extrac- tion operations, no extra errors will be introduced into the radiomics analysis because of feature calculations. The effectiveness of features is related only to the quality of segmentation. If the tumor has been segmented precisely, the accuracy and effectiveness of the image features can be guaranteed 解释

这段话提到了一个新的医学影像分析方法，叫做基于深度学习的放射组学（DLR）。与现有的放射组学方法不同，DLR直接从深度神经网络中提取高通量的图像特征，而不需要进行额外的特征提取操作，从而避免了因特征计算而引入额外的误差。DLR的主要假设是，一旦图像被深度神经网络准确地分割出来，所有与分割区域相关的信息都已经被嵌入到网络中。因此，DLR的特征有效性与分割的质量密切相关。如果肿瘤被准确地分割了，那么图像特征的准确性和有效性就能得到保证。

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We consider a persuasion problem between a sender and a receiver where utility may be nonlinear in the latter’s belief; we call such receivers risk conscious. Such utility models arise when the receiver exhibits systematic biases away from expected utility maximization, such as uncertainty aversion (e.g., from sensitivity to the variance of the waiting time for a service). Because of this nonlinearity, the standard approach to finding the optimal persuasion mechanism using revelation principle fails. To overcome this difficulty, we use the underlying geometry of the problem to develop a convex optimization framework to find the optimal persuasion mechanism. We define the notion of full persuasion and use our framework to characterize conditions under which full persuasion can be achieved. We use our approach to study binary persuasion, where the receiver has two actions and the sender strictly prefers one of them at every state. Under a convexity assumption, we show that the binary persuasion problem reduces to a linear program and establish a canonical set of signals where each signal either reveals the state or induces in the receiver uncertainty between two states. Finally, we discuss the broader applicability of our methods to more general contexts, and we illustrate our methodology by studying information sharing of waiting times in service systems翻译一下

我们考虑一个发件人和接收者之间的说服问题，其中接收者的效用可能在其信念中是非线性的；我们把这样的接收者称为风险感知者。这种效用模型出现在接收者偏离预期效用最大化的系统性偏差中，例如不确定性规避（例如，对于等待服务的等待时间方差的敏感性）。由于这种非线性，使用启示原则寻找最优说服机制的标准方法失败了。为了克服这个难点，我们利用问题的基本几何形状，开发了一个凸优化框架来找到最优的说服机制。我们定义了完全说服的概念，并使用我们的框架来表征实现完全说服的条件。我们使用我们的方法研究了二元说服问题，其中接收者有两个行动，发件人在每个状态下都严格偏好其中一个。在凸性假设下，我们证明了二元说服问题可以简化为线性规划，并建立了一个规范信号集，其中每个信号要么揭示状态，要么在接收者两个状态之间引起不确定性。最后，我们讨论了我们的方法对更一般情境的广泛适用性，并通过研究服务系统中等待时间的信息共享来说明我们的方法论。

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