Giorgos Papanastasiou | DeepAI

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∙ 08/03/2023

Explainable unsupervised multi-modal image registration using deep networks

Clinical decision making from magnetic resonance imaging (MRI) combines ...

0 Chengjia Wang, et al. ∙

∙ 07/24/2023

Is attention all you need in medical image analysis? A review

Medical imaging is a key component in clinical diagnosis, treatment plan...

0 Giorgos Papanastasiou, et al. ∙

∙ 03/19/2023

Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations

As a pragmatic data augmentation tool, data synthesis has generally retu...

0 Xiaodan Xing, et al. ∙

∙ 03/07/2022

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

Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (...

57 Chengjia Wang, et al. ∙

∙ 09/05/2020

Semi-supervised Pathology Segmentation with Disentangled Representations

Automated pathology segmentation remains a valuable diagnostic tool in c...

9 Haochuan Jiang, et al. ∙

∙ 11/11/2019

Disentangle, align and fuse for multimodal and zero-shot image segmentation

Magnetic resonance (MR) protocols rely on several sequences to properly ...

40 Agisilaos Chartsias, et al. ∙

∙ 07/11/2019

FIRE: Unsupervised bi-directional inter-modality registration using deep networks

Inter-modality image registration is an critical preprocessing step for ...

0 Chengjia Wang, et al. ∙

∙ 03/22/2019

Factorised Representation Learning in Cardiac Image Analysis

Typically, a medical image offers spatial information on the anatomy (an...

0 Agisilaos Chartsias, et al. ∙

∙ 08/12/2018

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

Recently, the cycle-consistent generative adversarial networks (CycleGAN...

0 Chengjia Wang, et al. ∙

∙ 03/19/2018

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

The success and generalisation of deep learning algorithms heavily depen...

0 Agisilaos Chartsias, et al. ∙

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