5 papers
Semi-supervised Pathology Segmentation with Disentangled Representations
Haochuan Jiang, Agisilaos Chartsias, Xinheng Zhang +6
Automated pathology segmentation remains a valuable diagnostic tool in clinical practice. However, collecting training data is challenging. Semi-supervised approaches by combining…
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
Agisilaos Chartsias, Giorgos Papanastasiou, Chengjia Wang +4
Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here…
Disentangled Representation Learning in Cardiac Image Analysis
Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou +4
Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Reson…
A Distance Oriented Kalman Filter Particle Swarm Optimizer Applied to Multi-Modality Image Registration
Chengjia Wang, Keith A. Goatman, James Boardman +3
In this paper we describe improvements to the particle swarm optimizer (PSO) made by inclusion of an unscented Kalman filter to guide particle motion. We demonstrate the effectiven…
Factorised spatial representation learning: application in semi-supervised myocardial segmentation
Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou +5
The success and generalisation of deep learning algorithms heavily depend on learning good feature representations. In medical imaging this entails representing anatomical informat…