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MACHINE-LEARNT PREDICTION OF UNCERTAINTY OR SENSITIVITY FOR HEMODYNAMIC QUANTIFICATION IN MEDICAL IMAGING
MACHINE-LEARNT PREDICTION OF UNCERTAINTY OR SENSITIVITY FOR HEMODYNAMIC QUANTIFICATION IN MEDICAL IMAGING
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机译:基于机器学习的医学成像中血流动力学定量的不确定性或敏感性预测
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摘要
The uncertainty, sensitivity, and/or standard deviation for a patient-specific hemodynamic quantification is determined. The contribution of different information, such as the fit of the geometry at different locations, to the uncertainty or sensitivity is determined. Alternatively or additionally, the amount of contribution of information at one location (e.g., geometric fit at the one location) to uncertainty or sensitivity at other locations is determined. Rather than relying on time consuming statistical analysis for each patient, a machine-learnt classifier is trained to determine the uncertainty, sensitivity, and/or standard deviation for the patient.
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