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08/15/2023
Riemannian geometry for efficient analysis of protein dynamics data
An increasingly common viewpoint is that protein dynamics data sets resi...
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05/25/2023
Neural incomplete factorization: learning preconditioners for the conjugate gradient method
In this paper, we develop a novel data-driven approach to accelerate sol...
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09/20/2022
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation
Uncertainty quantification in automated image analysis is highly desired...
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09/12/2022
Spectral decomposition of atomic structures in heterogeneous cryo-EM
We consider the problem of recovering the three-dimensional atomic struc...
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08/05/2022
Deep Learning for Material Decomposition in Photon-Counting CT
Photon-counting CT (PCCT) offers improved diagnostic performance through...
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06/11/2022
Learned reconstruction with convergence guarantees
In recent years, deep learning has achieved remarkable empirical success...
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05/24/2022
3D helical CT reconstruction with memory efficient invertible Learned Primal-Dual method
Helical acquisition geometry is the most common geometry used in compute...
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08/26/2021
Deep learning based dictionary learning and tomographic image reconstruction
This work presents an approach for image reconstruction in clinical low-...
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08/12/2021
Deep Microlocal Reconstruction for Limited-Angle Tomography
We present a deep learning-based algorithm to jointly solve a reconstruc...
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06/07/2021
End-to-end reconstruction meets data-driven regularization for inverse problems
We propose an unsupervised approach for learning end-to-end reconstructi...
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03/30/2021
Adversarially learned iterative reconstruction for imaging inverse problems
In numerous practical applications, especially in medical image reconstr...
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08/06/2020
Learned convex regularizers for inverse problems
We consider the variational reconstruction framework for inverse problem...
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07/20/2020
Image reconstruction in dynamic inverse problems with temporal models
The paper surveys variational approaches for image reconstruction in dyn...
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11/27/2019
Shearlets as Feature Extractor for Semantic Edge Detection: The Model-Based and Data-Driven Realm
Semantic edge detection has recently gained a lot of attention as an ima...
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08/26/2019
Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation
Patient movement in emission tomography deteriorates reconstruction qual...
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08/01/2019
Multi-Scale Learned Iterative Reconstruction
Model-based learned iterative reconstruction methods have recently been ...
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01/05/2019
Extraction of digital wavefront sets using applied harmonic analysis and deep neural networks
Microlocal analysis provides deep insight into singularity structures an...
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12/09/2018
A New Variational Model for Joint Image Reconstruction and Motion Estimation in Spatiotemporal Imaging
We propose a new variational model for joint image reconstruction and mo...
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11/14/2018
Deep Bayesian Inversion
Characterizing statistical properties of solutions of inverse problems i...
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08/27/2018
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model ...
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05/29/2018
Adversarial Regularizers in Inverse Problems
Inverse Problems in medical imaging and computer vision are traditionall...
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10/30/2017
Learning to solve inverse problems using Wasserstein loss
We propose using the Wasserstein loss for training in inverse problems. ...
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07/20/2017
Learned Primal-dual Reconstruction
We propose the Learned Primal-Dual algorithm for tomographic reconstruct...
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06/13/2017
Indirect Image Registration with Large Diffeomorphic Deformations
The paper adapts the large deformation diffeomorphic metric mapping fram...
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04/13/2017
