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Image reconstruction from projections of digital breast tomosynthesis using deep learning
(2021-01-01)
The Filtered Backprojection (FBP) algorithm for Computed Tomography (CT) reconstruction can be mapped entire in an Artificial Neural Network (ANN), with the backprojection (BP) operation simulated analytically in a layer ...
End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA
(2021)
Purpose: To develop an end-to-end deep learning technique for nonrigid motion-corrected (MoCo) reconstruction of ninefold undersampled free-breathing whole-heart coronary MRA (CMRA).
Temperature-Based Deep Boltzmann Machines
(Springer, 2018-08-01)
Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications, that range from speech recognition to face-based user identification. Despite other ...
Fine Tuning Deep Boltzmann Machines Through Meta-Heuristic Approaches
(Ieee, 2018-01-01)
The Deep learning framework has been widely used in different applications from medicine to engineering. However, there is a lack of works that manage to deal with the issue of hyperparameter fine-tuning, since machine ...
Self-supervised motion-corrected image reconstruction network for 4D magnetic resonance imaging of the body trunk
(Now Publishers Inc, 2022)
Respiratory motion can cause artifacts in magnetic resonance imaging of the body trunk if patients cannot hold their breath or triggered acquisitions are not practical. Retrospective correction strategies usually cope with ...
DeepSPIO: Super paramagnetic iron oxide particle quantification using deep learning in magnetic resonance imaging
(2019)
The susceptibility of Super Paramagnetic Iron Oxide (SPIO) particles makes them a useful contrast agent for different purposes in MRI. These particles are typically quantified with relaxometry or by measuring the inhomogeneities ...
A metaheuristic-driven approach to fine-tune Deep Boltzmann Machines
(Elsevier B.V., 2020-12-01)
Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable range of domains. One of the main shortcomings ...
Fine-Tuning Dropout Regularization in Energy-Based Deep Learning
(2021-01-01)
Deep Learning architectures have been extensively studied in the last years, mainly due to their discriminative power in Computer Vision. However, one problem related to such models concerns their number of parameters and ...
DeepSPIO: Super Paramagnetic Iron Oxide Particle Quantification using Deep Learning in Magnetic Resonance Imaging
(2020)
The susceptibility of Super Paramagnetic Iron Oxide (SPIO) particles makes them a useful contrast agent for different purposes in MRI. These particles are typically quantified with relaxometry or by measuring the inhomogeneities ...
AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design
(Institute of Electrical and Electronics Engineers Inc.PE, 2021)
The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting ...