Search Results for author:
Found 5 papers, 3 papers with code
Frequency-Time Diffusion with Neural Cellular Automata
Denoising Diffusion Models (DDMs) have become the leading generative technique for synthesizing high-quality images but are often constrained by their UNet-based architectures that impose certain limitations.
M3D-NCA: Robust 3D Segmentation with Built-in Quality Control
Medical image segmentation relies heavily on large-scale deep learning models, such as UNet-based architectures.
Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata
Access to the proper infrastructure is critical when performing medical image segmentation with Deep Learning.
Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation
In cardiovascular magnetic resonance imaging (CMR), respiratory motion represents a major challenge in terms of acquisition quality and therefore subsequent analysis and final diagnosis.
Disentanglement enables cross-domain Hippocampus Segmentation
This separation enables us to perform a domain transfer and thus convert data from new sources into the training domain.
