no code implementations • 6 Aug 2021 • Agisilaos Chartsias, Shan Gao, Angela Mumith, Jorge Oliveira, Kanwal Bhatia, Bernhard Kainz, Arian Beqiri
Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function.
1 code implementation • 5 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.
1 code implementation • 5 Sep 2020 • Haochuan Jiang, Chengjia Wang, Agisilaos Chartsias, Sotirios A. Tsaftaris
Together with the corresponding encoding features, these representations are propagated to decoding layers with U-Net skip-connections.
4 code implementations • 27 Aug 2020 • Xiao Liu, Spyridon Thermos, Gabriele Valvano, Agisilaos Chartsias, Alison O'Neil, Sotirios A. Tsaftaris
In this paper, we conduct an empirical study to investigate the role of different biases in content-style disentanglement settings and unveil the relationship between the degree of disentanglement and task performance.
1 code implementation • 26 Aug 2020 • Xiao Liu, Spyridon Thermos, Agisilaos Chartsias, Alison O'Neil, Sotirios A. Tsaftaris
Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains.
1 code implementation • 20 Apr 2020 • Tian Xia, Agisilaos Chartsias, Sotirios A. Tsaftaris
In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy.
1 code implementation • 4 Dec 2019 • Tian Xia, Agisilaos Chartsias, Chengjia Wang, Sotirios A. Tsaftaris
Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer's Disease (AD, an ordinal variable).
2 code implementations • 11 Nov 2019 • Agisilaos Chartsias, Giorgos Papanastasiou, Chengjia Wang, Scott Semple, David E. Newby, Rohan Dharmakumar, Sotirios A. Tsaftaris
Core to our method is learning a disentangled decomposition into anatomical and imaging factors.
1 code implementation • 29 Aug 2019 • Gabriele Valvano, Agisilaos Chartsias, Andrea Leo, Sotirios A. Tsaftaris
There has been an increasing focus in learning interpretable feature representations, particularly in applications such as medical image analysis that require explainability, whilst relying less on annotated data (since annotations can be tedious and costly).
no code implementations • 11 Jul 2019 • Chengjia Wang, Giorgos Papanastasiou, Agisilaos Chartsias, Grzegorz Jacenkow, Sotirios A. Tsaftaris, Heye Zhang
Inter-modality image registration is an critical preprocessing step for many applications within the routine clinical pathway.
4 code implementations • 22 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.
no code implementations • 10 Jan 2019 • Tian Xia, Agisilaos Chartsias, Sotirios A. Tsaftaris
Pseudo healthy synthesis, i. e. the creation of a subject-specific `healthy' image from a pathological one, could be helpful in tasks such as anomaly detection, understanding changes induced by pathology and disease or even as data augmentation.
1 code implementation • 19 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.