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Found 8 papers, 0 papers with code
DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5D Diffusion Model
We introduce a new framework called DiffGEPCI for cross-modality generation in magnetic resonance imaging (MRI) using a 2. 5D conditional diffusion model.
A Structured Pruning Algorithm for Model-based Deep Learning
There is a growing interest in model-based deep learning (MBDL) for solving imaging inverse problems.
A Restoration Network as an Implicit Prior
Image denoisers have been shown to be powerful priors for solving inverse problems in imaging.
Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees
Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data.
SPICE: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation
However, estimation of accurate CSMs is a challenging problem when measurements are highly undersampled.
Deep Model-Based Architectures for Inverse Problems under Mismatched Priors
While the empirical performance and theoretical properties of DMBAs have been widely investigated, the existing work in the area has primarily focused on their performance when the desired image prior is known exactly.
Monotonically Convergent Regularization by Denoising
Regularization by denoising (RED) is a widely-used framework for solving inverse problems by leveraging image denoisers as image priors.
Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks
However, CNN in previous studies do not involve cell morphological information, and it is unknown whether morphological features can be directly modeled by CNN to classify cervical cells.
