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Found 5 papers, 3 papers with code
Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction
Firstly, we propose a single optimisation problem that simultaneously computes the MRI reconstruction and the physical motion.
Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding
In this work we propose a framework for joint optimization of the MRI reconstruction and lossy compression, producing compressed representations of medical images that achieve improved trade-offs between quality and bit-rate.
Learning the Sampling Pattern for MRI
The discovery of the theory of compressed sensing brought the realisation that many inverse problems can be solved even when measurements are "incomplete".
Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels
We propose WS-DINO as a novel framework to use weak label information in learning phenotypic representations from high-content fluorescent images of cells.
Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels
Image-to-image reconstruction problems with free or inexpensive metadata in the form of class labels appear often in biological and medical image domains.
