6 citations · 8 across the 8 of their papers we have counts for
8 papers
Compositional Segmentation of Cardiac Images Leveraging Metadata
Abbas Khan, Muhammad Asad, Martin Benning +2
Cardiac image segmentation is essential for automated cardiac function assessment and monitoring of changes in cardiac structures over time. Inspired by coarse-to-fine approaches i…
A lifted Bregman strategy for training unfolded proximal neural network Gaussian denoisers
Xiaoyu Wang, Martin Benning, Audrey Repetti
Unfolded proximal neural networks (PNNs) form a family of methods that combines deep learning and proximal optimization approaches. They consist in designing a neural network for a…
Improving Interpretability and Robustness for the Detection of AI-Generated Images
Tatiana Gaintseva, Laida Kushnareva, German Magai +5
With growing abilities of generative models, artificial content detection becomes an increasingly important and difficult task. However, all popular approaches to this problem suff…
Multi-view Cardiac Image Segmentation via Trans-Dimensional Priors
Abbas Khan, Muhammad Asad, Martin Benning +2
We propose a novel multi-stage trans-dimensional architecture for multi-view cardiac image segmentation. Our method exploits the relationship between long-axis (2D) and short-axis…
Crop and Couple: cardiac image segmentation using interlinked specialist networks
Abbas Khan, Muhammad Asad, Martin Benning +2
Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation. We propose a novel strategy that performs segmentation…
A Lifted Bregman Formulation for the Inversion of Deep Neural Networks
Xiaoyu Wang, Martin Benning
We propose a novel framework for the regularised inversion of deep neural networks. The framework is based on the authors' recent work on training feed-forward neural networks with…