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Found 15 papers, 1 papers with code
Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction
In this study we propose an extension of the Learned Primal-Dual algorithm for digital breast tomosynthesis.
Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation
We analyze two transfer learning settings: 1) unsupervised transfer, where Hologic data with soft lesion annotation at pixel level and Siemens unlabelled data are used to annotate images in the latter data; 2) weak supervised transfer, where exam level labels for images from the Siemens mammograph are available.
Kernel of CycleGAN as a Principle homogeneous space
It is known that the CycleGAN problem might admit multiple solutions, and our goal in this paper is to analyze the space of exact solutions and to give perturbation bounds for approximate solutions.
Inferring astrophysical X-ray polarization with deep learning
We investigate the use of deep learning in the context of X-ray polarization detection from astrophysical sources as will be observed by the Imaging X-ray Polarimetry Explorer (IXPE), a future NASA selected space-based mission expected to be operative in 2021.
Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation
This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks.
Kernel of CycleGAN as a principal homogeneous space
It is known that the CycleGAN problem might admit multiple solutions, and our goal in this paper is to analyze the space of exact solutions and to give perturbation bounds for approximate solutions.
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process.
Subpixel object segmentation using wavelets and multiresolution analysis
The effectiveness of the proposed method is demonstrated by delineating boundaries of simply connected domains (organs) in medical images using Debauches wavelets and comparing performance with a U-Net baseline.
Subpixel object segmentation using wavelets and multi resolution analysis
The effectiveness of the proposed method is demonstrated by delineating boundaries of simply connected domains (organs) in medical images using Debauches wavelets and comparing performance with a U-Net baseline.
Neural Modulation Fields for Conditional Cone Beam Neural Tomography
Conventional Computed Tomography (CT) methods require large numbers of noise-free projections for accurate density reconstructions, limiting their applicability to the more complex class of Cone Beam Geometry CT (CBCT) reconstruction.
Improving Lesion Volume Measurements on Digital Mammograms
Finally, for a subset of 100 mammograms with a malign mass and concurrent MRI examination available, we analyze the agreement between lesion volume on mammography and MRI, resulting in an intraclass correlation coefficient of 0. 81 [95% CI 0. 73 - 0. 87] for consistency and 0. 78 [95% CI 0. 66 - 0. 86] for absolute agreement.
vSHARP: variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse-Problems
In this study, we propose vSHARP (variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse Problems), a novel DL-based method for solving ill-posed inverse problems arising in MI.
Deep Cardiac MRI Reconstruction with ADMM
In this work, inspired by related work in accelerated MRI reconstruction, we present a deep learning (DL)-based method for accelerated cine and multi-contrast reconstruction in the context of dynamic cardiac imaging.
JSSL: Joint Supervised and Self-supervised Learning for MRI Reconstruction
In this paper, we introduce JSSL (Joint Supervised and Self-supervised Learning), a novel training approach for deep learning-based MRI reconstruction algorithms aimed at enhancing reconstruction quality in scenarios where target dataset(s) containing fully sampled k-space measurements are unavailable.
Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT
Our method surpasses classical and deep learning baselines, including LIRE, on the thorax test set.
