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Found 12 papers, 5 papers with code
Multi-view Cardiac Image Segmentation via Trans-Dimensional Priors
We evaluate our proposed method on the Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI (M&Ms-2) dataset, where our method outperforms state-of-the-art methods in segmenting cardiac regions of interest in both short-axis and long-axis images.
RAVE: Residual Vector Embedding for CLIP-Guided Backlit Image Enhancement
Instead, based on CLIP embeddings of backlit and well-lit images from training data, we compute the residual vector in the embedding space as a simple difference between the mean embeddings of the well-lit and backlit images.
Crop and Couple: cardiac image segmentation using interlinked specialist networks
Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation.
A Lifted Bregman Formulation for the Inversion of Deep Neural Networks
We propose a novel framework for the regularised inversion of deep neural networks.
Convergent Data-driven Regularizations for CT Reconstruction
The reconstruction of images from their corresponding noisy Radon transform is a typical example of an ill-posed linear inverse problem as arising in the application of computerized tomography (CT).
Lifted Bregman Training of Neural Networks
Instead of estimating the parameters with a combination of first-order optimisation method and back-propagation (as is the state-of-the-art), we propose the use of non-smooth first-order optimisation methods that exploit the specific structure of the novel formulation.
Timbre Transfer with Variational Auto Encoding and Cycle-Consistent Adversarial Networks
This research project investigates the application of deep learning to timbre transfer, where the timbre of a source audio can be converted to the timbre of a target audio with minimal loss in quality.
Generalised Perceptron Learning
We present a generalisation of Rosenblatt's traditional perceptron learning algorithm to the class of proximal activation functions and demonstrate how this generalisation can be interpreted as an incremental gradient method applied to a novel energy function.
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".
Deep learning as optimal control problems: models and numerical methods
We review the first order conditions for optimality, and the conditions ensuring optimality after discretisation.
Nonlinear Spectral Image Fusion
In this paper we demonstrate that the framework of nonlinear spectral decompositions based on total variation (TV) regularization is very well suited for image fusion as well as more general image manipulation tasks.
Variational Depth from Focus Reconstruction
This paper deals with the problem of reconstructing a depth map from a sequence of differently focused images, also known as depth from focus or shape from focus.
