Search Results for author: Giorgos Papanastasiou

Found 7 papers, 4 papers with code

Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis

no code implementations7 Mar 2022 Chengjia Wang, Guang Yang, Giorgos Papanastasiou

Moreover, inverse-consistency is a fundamental inter-modality registration property that is not considered in deep learning registration algorithms.

Image Generation Image Registration

Semi-supervised Pathology Segmentation with Disentangled Representations

1 code implementation5 Sep 2020 Haochuan Jiang, Agisilaos Chartsias, Xinheng Zhang, Giorgos Papanastasiou, Scott Semple, Mark Dweck, David Semple, Rohan Dharmakumar, Sotirios A. Tsaftaris

The model is trained in a semi-supervised fashion with new reconstruction losses directly aiming to improve pathology segmentation with limited annotations.

Anatomy Disentanglement

Disentangled Representation Learning in Cardiac Image Analysis

4 code implementations22 Mar 2019 Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou, Michelle Williams, David Newby, Rohan Dharmakumar, Sotirios A. Tsaftaris

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.

Anatomy Computed Tomography (CT) +2

Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks

no code implementations12 Aug 2018 Chengjia Wang, Gillian Macnaught, Giorgos Papanastasiou, Tom MacGillivray, David Newby

Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images.

Image Generation

Factorised spatial representation learning: application in semi-supervised myocardial segmentation

1 code implementation19 Mar 2018 Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou, Scott Semple, Michelle Williams, David Newby, Rohan Dharmakumar, Sotirios A. Tsaftaris

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.

Medical Image Segmentation Myocardium Segmentation +1

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