From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling

• URL: http://arxiv.org/abs/2302.05304v1
• Date: Fri, 10 Feb 2023 15:05:09 GMT
• Title: From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling
• Authors: Jan Ernsting, Nils R. Winter, Ramona Leenings, Kelvin Sarink, Carlotta B. C. Barkhau, Lukas Fisch, Daniel Emden, Vincent Holstein, Jonathan Repple, Dominik Grotegerd, Susanne Meinert, NAKO Investigators, Klaus Berger, Benjamin Risse, Udo Dannlowski, Tim Hahn
• Abstract summary: Brain-age gap is one of the most investigated risk markers for brain changes across disorders. We combine uncertainty-aware deep Neural Networks with conformal prediction theory. We show in a sample of N=16,794 participants that a lower or comparable error as state-of-the-art, large-scale brain-age models. The higher individual probabilities of accelerated brain-aging derived from our model are associated with Alzheimer's Disease, Bipolar Disorder and Major Depressive Disorder.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The brain-age gap is one of the most investigated risk markers for brain changes across disorders. While the field is progressing towards large-scale models, recently incorporating uncertainty estimates, no model to date provides the single-subject risk assessment capability essential for clinical application. In order to enable the clinical use of brain-age as a biomarker, we here combine uncertainty-aware deep Neural Networks with conformal prediction theory. This approach provides statistical guarantees with respect to single-subject uncertainty estimates and allows for the calculation of an individual's probability for accelerated brain-aging. Building on this, we show empirically in a sample of N=16,794 participants that 1. a lower or comparable error as state-of-the-art, large-scale brain-age models, 2. the statistical guarantees regarding single-subject uncertainty estimation indeed hold for every participant, and 3. that the higher individual probabilities of accelerated brain-aging derived from our model are associated with Alzheimer's Disease, Bipolar Disorder and Major Depressive Disorder.

Related papers

• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• A personalized Uncertainty Quantification framework for patient survival models: estimating individual uncertainty of patients with metastatic brain tumors in the absence of ground truth [4.665141823455397]
  We developed and evaluated our approach based on a dataset of 1383 patients treated with stereotactic radiosurgery for brain metastases. Our results show that all models had the lowest uncertainty on ICP (2.21%) and the highest uncertainty (17.28%) on ICPD.
  arXiv  Detail & Related papers  (2023-11-28T19:07:30Z)
• Beyond Voxel Prediction Uncertainty: Identifying brain lesions you can trust [1.1199585259018459]
  Deep neural networks have become the gold-standard approach for the automated segmentation of 3D medical images. In this work, we propose to go beyond voxel-wise assessment using an innovative Graph Neural Network approach. This network allows the fusion of three estimators of voxel uncertainty: entropy, variance, and model's confidence.
  arXiv  Detail & Related papers  (2022-09-22T09:20:05Z)
• Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing [16.397774179084607]
  We propose a flexible and scalable approach based on the concept of permutation testing. We apply our approach to the yearly self-reported assessments of 621 adolescent participants of the National Consortium of Alcohol and Neurodevelopment in Adolescence. Our method successfully identifies categories of risk factors that further explain the symptom.
  arXiv  Detail & Related papers  (2022-07-28T19:01:09Z)
• Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model [68.34559610536614]
  We argue that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model. We propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation. Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
  arXiv  Detail & Related papers  (2021-11-22T08:54:10Z)
• An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling [0.45763926712997843]
  We introduce an uncertainty-aware, shareable, and transparent Monte-Carlo Dropout Composite-Quantile-Regression Neural Network. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data. It achieves lower error rates compared to existing models across ten recruitment centers and in three independent validation samples.
  arXiv  Detail & Related papers  (2021-07-16T15:48:08Z)
• DEUP: Direct Epistemic Uncertainty Prediction [56.087230230128185]
  Epistemic uncertainty is part of out-of-sample prediction error due to the lack of knowledge of the learner. We propose a principled approach for directly estimating epistemic uncertainty by learning to predict generalization error and subtracting an estimate of aleatoric uncertainty.
  arXiv  Detail & Related papers  (2021-02-16T23:50:35Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [93.58854458951431]
  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
  arXiv  Detail & Related papers  (2020-09-16T15:16:03Z)
• A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
  We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models. We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
  arXiv  Detail & Related papers  (2020-06-08T19:24:54Z)
• Statistical Agnostic Mapping: a Framework in Neuroimaging based on Concentration Inequalities [0.0]
  We derive a Statistical Agnostic (non-parametric) Mapping at voxel or multi-voxel level. We propose a novel framework in neuroimaging based on concentration inequalities.
  arXiv  Detail & Related papers  (2019-12-27T18:27:50Z)

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.