Latent linear dynamics in spatiotemporal medical data

• URL: http://arxiv.org/abs/2103.00930v1
• Date: Mon, 1 Mar 2021 11:42:21 GMT
• Title: Latent linear dynamics in spatiotemporal medical data
• Authors: Niklas Gunnarsson, Jens Sj\"olund and Thomas B. Sch\"on
• Abstract summary: We present an unsupervised model that identifies the underlying dynamics of the system, only based on the sequential images. The model maps the input to a low-dimensional latent space wherein a linear relationship holds between a hidden state process and the observed latent process. Knowledge of the system dynamics enables denoising, imputation of missing values and extrapolation of future image frames.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatiotemporal imaging is common in medical imaging, with applications in e.g. cardiac diagnostics, surgical guidance and radiotherapy monitoring. In this paper, we present an unsupervised model that identifies the underlying dynamics of the system, only based on the sequential images. The model maps the input to a low-dimensional latent space wherein a linear relationship holds between a hidden state process and the observed latent process. Knowledge of the system dynamics enables denoising, imputation of missing values and extrapolation of future image frames. We use a Variational Auto-Encoder (VAE) for the dimensionality reduction and a Linear Gaussian State Space Model (LGSSM) for the latent dynamics. The model, known as a Kalman Variational Auto-Encoder, is end-to-end trainable and the weights, both in the VAE and LGSSM, are simultaneously updated by maximizing the evidence lower bound of the marginal log likelihood. Our experiment, on cardiac ultrasound time series, shows that the dynamical model provide better reconstructions than a similar model without dynamics. And also possibility to impute and extrapolate for missing samples.

Related papers

• A scalable generative model for dynamical system reconstruction from neuroimaging data [5.777167013394619]
  Data-driven inference of the generative dynamics underlying a set of observed time series is of growing interest in machine learning. Recent breakthroughs in training techniques for state space models (SSMs) specifically geared toward dynamical systems reconstruction (DSR) enable to recover the underlying system. We propose a novel algorithm that solves this problem and scales exceptionally well with model dimensionality and filter length.
  arXiv  Detail & Related papers  (2024-11-05T09:45:57Z)
• Latent Space Energy-based Neural ODEs [73.01344439786524]
  This paper introduces a novel family of deep dynamical models designed to represent continuous-time sequence data. We train the model using maximum likelihood estimation with Markov chain Monte Carlo. Experiments on oscillating systems, videos and real-world state sequences (MuJoCo) illustrate that ODEs with the learnable energy-based prior outperform existing counterparts.
  arXiv  Detail & Related papers  (2024-09-05T18:14:22Z)
• Physics-informed graph neural networks for flow field estimation in carotid arteries [2.0437999068326276]
  Hemodynamic quantities are valuable biomedical risk factors for cardiovascular pathology such as atherosclerosis. In this work, we create a surrogate model for hemodynamic flow field estimation, powered by machine learning. We train graph neural networks that include priors about the underlying symmetries and physics, limiting the amount of data required for training. This shows that physics-informed graph neural networks can be trained using 4D flow MRI data to estimate blood flow in unseen carotid artery geometries.
  arXiv  Detail & Related papers  (2024-08-13T13:09:28Z)
• KFD-NeRF: Rethinking Dynamic NeRF with Kalman Filter [49.85369344101118]
  We introduce KFD-NeRF, a novel dynamic neural radiance field integrated with an efficient and high-quality motion reconstruction framework based on Kalman filtering. Our key idea is to model the dynamic radiance field as a dynamic system whose temporally varying states are estimated based on two sources of knowledge: observations and predictions. Our KFD-NeRF demonstrates similar or even superior performance within comparable computational time and state-of-the-art view synthesis performance with thorough training.
  arXiv  Detail & Related papers  (2024-07-18T05:48:24Z)
• Neural Ordinary Differential Equation based Sequential Image Registration for Dynamic Characterization [13.492983263194636]
  This extension work discusses how this framework can aid in the characterization of sequential biological processes. Our framework considers voxels as particles within a dynamic system, defining deformation fields through the integration of neural differential equations. We evaluated our framework on two clinical datasets: one for cardiac motion tracking and another for longitudinal brain MRI analysis.
  arXiv  Detail & Related papers  (2024-04-02T17:04:45Z)
• Individualized Dosing Dynamics via Neural Eigen Decomposition [51.62933814971523]
  We introduce the Neural Eigen Differential Equation algorithm (NESDE) NESDE provides individualized modeling, tunable generalization to new treatment policies, and fast, continuous, closed-form prediction. We demonstrate the robustness of NESDE in both synthetic and real medical problems, and use the learned dynamics to publish simulated medical gym environments.
  arXiv  Detail & Related papers  (2023-06-24T17:01:51Z)
• Analysis of Numerical Integration in RNN-Based Residuals for Fault Diagnosis of Dynamic Systems [0.6999740786886536]
  The paper includes a case study of a heavy-duty truck's after-treatment system to highlight the potential of these techniques for improving fault diagnosis performance. Data-driven modeling and machine learning are widely used to model the behavior of dynamic systems.
  arXiv  Detail & Related papers  (2023-05-08T12:48:18Z)
• Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models [1.6352599467675781]
  We propose a method based on diffusion models to detect and segment anomalies in brain imaging. Our diffusion models achieve competitive performance compared with autoregressive approaches across a series of experiments with 2D CT and MRI data.
  arXiv  Detail & Related papers  (2022-06-07T17:30:43Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression [71.7560927415706]
  latent hybridisation model (LHM) integrates a system of expert-designed ODEs with machine-learned Neural ODEs to fully describe the dynamics of the system. We evaluate LHM on synthetic data as well as real-world intensive care data of COVID-19 patients.
  arXiv  Detail & Related papers  (2021-06-05T11:42:45Z)
• Dynamic Mode Decomposition in Adaptive Mesh Refinement and Coarsening Simulations [58.720142291102135]
  Dynamic Mode Decomposition (DMD) is a powerful data-driven method used to extract coherent schemes. This paper proposes a strategy to enable DMD to extract from observations with different mesh topologies and dimensions.
  arXiv  Detail & Related papers  (2021-04-28T22:14:25Z)

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.