Diffusion Tensor Estimation with Uncertainty Calibration

• URL: http://arxiv.org/abs/2111.10847v3
• Date: Tue, 27 Aug 2024 12:09:32 GMT
• Title: Diffusion Tensor Estimation with Uncertainty Calibration
• Authors: Davood Karimi, Simon K. Warfield, Ali Gholipour,
• Abstract summary: We propose a deep learning method to estimate the diffusion tensor and compute the estimation uncertainty. Data-dependent uncertainty is computed directly by the network and learned via loss attenuation. We show that the estimation uncertainties computed by the new method can highlight the model's biases, detect domain shift, and reflect the strength of noise in the measurements.
• Score: 6.5085381751712506
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: It is highly desirable to know how uncertain a model's predictions are, especially for models that are complex and hard to understand as in deep learning. Although there has been a growing interest in using deep learning methods in diffusion-weighted MRI, prior works have not addressed the issue of model uncertainty. Here, we propose a deep learning method to estimate the diffusion tensor and compute the estimation uncertainty. Data-dependent uncertainty is computed directly by the network and learned via loss attenuation. Model uncertainty is computed using Monte Carlo dropout. We also propose a new method for evaluating the quality of predicted uncertainties. We compare the new method with the standard least-squares tensor estimation and bootstrap-based uncertainty computation techniques. Our experiments show that when the number of measurements is small the deep learning method is more accurate and its uncertainty predictions are better calibrated than the standard methods. We show that the estimation uncertainties computed by the new method can highlight the model's biases, detect domain shift, and reflect the strength of noise in the measurements. Our study shows the importance and practical value of modeling prediction uncertainties in deep learning-based diffusion MRI analysis.

Related papers

• Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials [0.0]
  Uncertainty estimations for machine learning interatomic potentials are crucial to quantify the additional model error they introduce. We consider GPR models with Coulomb and SOAP representations as inputs to predict potential energy surfaces and excitation energies of molecules. We evaluate, how the GPR variance and ensemble-based uncertainties relate to the error and whether model performance improves by selecting the most uncertain samples from a fixed configuration space.
  arXiv  Detail & Related papers  (2024-10-27T10:06:09Z)
• One step closer to unbiased aleatoric uncertainty estimation [71.55174353766289]
  We propose a new estimation method by actively de-noising the observed data. By conducting a broad range of experiments, we demonstrate that our proposed approach provides a much closer approximation to the actual data uncertainty than the standard method.
  arXiv  Detail & Related papers  (2023-12-16T14:59:11Z)
• Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation [50.920911532133154]
  The intrinsic ill-posedness and ordinal-sensitive nature of monocular depth estimation (MDE) models pose major challenges to the estimation of uncertainty degree. We propose to model the uncertainty of MDE models from the perspective of the inherent probability distributions. By simply introducing additional training regularization terms, our model, with surprisingly simple formations and without requiring extra modules or multiple inferences, can provide uncertainty estimations with state-of-the-art reliability.
  arXiv  Detail & Related papers  (2023-07-19T12:11:15Z)
• The Implicit Delta Method [61.36121543728134]
  In this paper, we propose an alternative, the implicit delta method, which works by infinitesimally regularizing the training loss of uncertainty. We show that the change in the evaluation due to regularization is consistent for the variance of the evaluation estimator, even when the infinitesimal change is approximated by a finite difference.
  arXiv  Detail & Related papers  (2022-11-11T19:34:17Z)
• Data Uncertainty without Prediction Models [0.8223798883838329]
  We propose an uncertainty estimation method named a Distance-weighted Class Impurity without explicit use of prediction models. We verified that the Distance-weighted Class Impurity works effectively regardless of prediction models.
  arXiv  Detail & Related papers  (2022-04-25T13:26:06Z)
• Dense Uncertainty Estimation [62.23555922631451]
  In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation. We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
  arXiv  Detail & Related papers  (2021-10-13T01:23:48Z)
• Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression [30.112634874443494]
  We propose a new model that estimates uncertainty in a single forward pass. Our approach combines a bi-Lipschitz feature extractor with an inducing point approximate Gaussian process, offering robust and principled uncertainty estimation.
  arXiv  Detail & Related papers  (2021-02-22T23:29:12Z)
• The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals [51.71066839337174]
  Existing methods can quantify the error in the target estimation, but they tend to underestimate it. We propose a new separable formulation for the estimation of a signal and of its uncertainty, avoiding the effect of overfitting. We demonstrate that the proposed method outperforms a state-of-the-art technique for signal and uncertainty estimation.
  arXiv  Detail & Related papers  (2020-11-03T12:11:27Z)
• Learning to Predict Error for MRI Reconstruction [67.76632988696943]
  We demonstrate that predictive uncertainty estimated by the current methods does not highly correlate with prediction error. We propose a novel method that estimates the target labels and magnitude of the prediction error in two steps.
  arXiv  Detail & Related papers  (2020-02-13T15:55:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.