Sökning: "aleatoric uncertainty estimation"

Hittade 3 uppsatser innehållade orden aleatoric uncertainty estimation.

  1. 1. Uncertainty Estimation in Radiation Dose Prediction U-Net

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Frida Skarf; [2023]
    Nyckelord :Radiation dose prediction models; U-net; quantile regression; Monte Carlo Dropout; epistemic uncertainty estimation; aleatoric uncertainty estimation; Stråldospredicerande modeller; U-net; kvantilregression; Monte Carlo Dropout; epistemisk osäkerhetsskattning; aletorisk osäkerhetsskattning;

    Sammanfattning : The ability to quantify uncertainties associated with neural network predictions is crucial when they are relied upon in decision-making processes, especially in safety-critical applications like radiation therapy. In this paper, a single-model estimator of both epistemic and aleatoric uncertainties in a regression 3D U-net used for radiation dose prediction is presented. LÄS MER

  3. 2. Uncertainty Estimation in Volumetric Image Segmentation

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Donggyun Park; [2023]
    Nyckelord :Uncertainty Estimation; Uncertainty Quantification UQ ; Volumetric Image Segmentation; 3D U-Net; test-time data augmentation; Deep ensemble;

    Sammanfattning : The performance of deep neural networks and estimations of their robustness has been rapidly developed. In contrast, despite the broad usage of deep convolutional neural networks (CNNs)[1] for medical image segmentation, research on their uncertainty estimations is being far less conducted. LÄS MER

  4. 3. Real-time Uncertainty Estimation for Semantic Segmentation : Improving Uncertainty Estimates with Temperature Scaling and Predicted Dirichlet Distributions

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Lukas Grannas; [2020]
    Nyckelord :;

    Sammanfattning : This degree project examined different aspects of real-time uncertainty estimation for semantic segmentation deep learning networks in an autonomous driving setting. Two main tracks were taken. LÄS MER