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SYSTEM AND METHOD FOR QUANTIFYING UNCERTAINTY IN REASONING ABOUT 2D AND 3D SPATIAL FEATURES WITH A COMPUTER MACHINE LEARNING ARCHITECTURE

机译:用计算机机器学习架构对2D和3D空间特征进行推理的不确定性的系统和方法

摘要

This invention provides a system and method to propagate uncertainty information in a deep learning pipeline. It allows for the propagation of uncertainty information from one deep learning model to the next by fusing model uncertainty with the original imagery dataset. This approach results in a deep learning architecture where the output of the system contains not only the prediction, but also the model uncertainty information associated with that prediction. The embodiments herein improve upon existing deep learning-based models (CADe models) by providing the model with uncertainty/confidence information associated with (e.g. CADe) decisions. This uncertainty information can be employed in various ways, including (a) transmitting uncertainty from a first stage (or subsystem) of the machine learning system into a next (second) stage (or the next subsystem), and (b) providing uncertainty information to the end user in a manner that characterizes the uncertainty of the overall machine learning model.
机译:本发明提供了一种在深度学习管线中传播不确定性信息的系统和方法。通过将模型不确定性与原始图像数据集融合,可以将不确定性信息从一种深度学习模型传播到另一种深度学习模型。这种方法导致了深度学习架构,其中系统的输出不仅包含预测,还包含与该预测关联的模型不确定性信息。本文的实施例通过向模型提供与(例如,CADe)决策相关联的不确定性/置信度信息来改进现有的基于深度学习的模型(CADe模型)。该不确定性信息可以以多种方式使用,包括(a)将不确定性从机器学习系统的第一阶段(或子系统)传输到下一个(第二)阶段(或下一个子系统),以及(b)提供不确定性信息以表征整个机器学习模型不确定性的方式提供给最终用户。

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