20 citations · 24 across the 5 of their papers we have counts for
5 papers
: A Parameter-Efficient Foundation Model for Molecular Learning
Kerstin Kläser, Błażej Banaszewski, Samuel Maddrell-Mander +5
In biological tasks, data is rarely plentiful as it is generated from hard-to-gather measurements. Therefore, pre-training foundation models on large quantities of available data a…
The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties
Pedro Borges, Richard Shaw, Thomas Varsavsky +5
Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter depen…
Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework
Marta B. M. Ranzini, Irme Groothuis, Kerstin Kläser +5
Metal artefact reduction (MAR) techniques aim at removing metal-induced noise from clinical images. In Computed Tomography (CT), supervised deep learning approaches have been shown…
Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning
Kerstin Kläser, Thomas Varsavsky, Pawel Markiewicz +6
The ability to synthesise Computed Tomography images - commonly known as pseudo CT, or pCT - from MRI input data is commonly assessed using an intensity-wise similarity, such as an…
Deep Boosted Regression for MR to CT Synthesis
Kerstin Kläser, Pawel Markiewicz, Marta Ranzini +7
Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification. However, attenuation correction…