Search Results for author:
Found 7 papers, 4 papers with code
Low-field magnetic resonance image enhancement via stochastic image quality transfer
Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field.
Stable Optimization for Large Vision Model Based Deep Image Prior in Cone-Beam CT Reconstruction
In this paper, we propose a stable optimization method for the forward-model-free, LVM-based DIP model for sparse-view CBCT.
Progressive Subsampling for Oversampled Data -- Application to Quantitative MRI
We build upon a recent dual-network approach that won the MICCAI MUlti-DIffusion (MUDI) quantitative MRI measurement sampling-reconstruction challenge, but suffers from deep learning training instability, by subsampling with a hard decision boundary.
Continual Contrastive Learning for Image Classification
In this paper, we make the first attempt to tackle the catastrophic forgetting problem in the mainstream self-supervised methods, i. e., contrastive learning methods.
Learning to Address Intra-segment Misclassification in Retinal Imaging
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity.
Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients
1. 5T or 3T scanners are the current standard for clinical MRI, but low-field (<1T) scanners are still common in many lower- and middle-income countries for reasons of cost and robustness to power failures.
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator
In this paper we propose a probabilistic decimation simulator to improve robustness of model training.
