Search Results for author:
Found 7 papers, 4 papers with code
Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis
Moreover, inverse-consistency is a fundamental inter-modality registration property that is not considered in deep learning registration algorithms.
Semi-supervised Pathology Segmentation with Disentangled Representations
The model is trained in a semi-supervised fashion with new reconstruction losses directly aiming to improve pathology segmentation with limited annotations.
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
Core to our method is learning a disentangled decomposition into anatomical and imaging factors.
FIRE: Unsupervised bi-directional inter-modality registration using deep networks
Inter-modality image registration is an critical preprocessing step for many applications within the routine clinical pathway.
Disentangled Representation Learning in Cardiac Image Analysis
We can venture further and consider that a medical image naturally factors into some spatial factors depicting anatomy and factors that denote the imaging characteristics.
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images.
Factorised spatial representation learning: application in semi-supervised myocardial segmentation
Specifically, we achieve comparable performance to fully supervised networks using a fraction of labelled images in experiments on ACDC and a dataset from Edinburgh Imaging Facility QMRI.
