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