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Found 9 papers, 1 papers with code
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.
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.
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.
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.
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.
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.
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.
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.
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.
