Search Results for author:
Found 5 papers, 3 papers with code
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.
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.
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".
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.
