1 code implementation • 29 Mar 2024 • Thomas Melistas, Nikos Spyrou, Nefeli Gkouti, Pedro Sanchez, Athanasios Vlontzos, Giorgos Papanastasiou, Sotirios A. Tsaftaris
Counterfactual image generation is pivotal for understanding the causal relations of variables, with applications in interpretability and generation of unbiased synthetic data.
no code implementations • 18 Mar 2024 • Guangming Huang, Yunfei Long, Yingya Li, Giorgos Papanastasiou
This work presents a thorough scoping review on explainable and interpretable DL in healthcare NLP.
no code implementations • 3 Aug 2023 • Chengjia Wang, Giorgos Papanastasiou
Clinical decision making from magnetic resonance imaging (MRI) combines complementary information from multiple MRI sequences (defined as 'modalities').
no code implementations • 24 Jul 2023 • Giorgos Papanastasiou, Nikolaos Dikaios, Jiahao Huang, Chengjia Wang, Guang Yang
Attention and Transformer compartments (Transf/Attention) which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers.
no code implementations • 25 May 2023 • Xiaodan Xing, Federico Felder, Yang Nan, Giorgos Papanastasiou, Walsh Simon, Guang Yang
In addition, we have empirically demonstrated that the utility score does not require images with both high fidelity and high variety.
no code implementations • 19 Mar 2023 • Xiaodan Xing, Giorgos Papanastasiou, Simon Walsh, Guang Yang
To address these issues, in this work, we propose a novel strategy for medical image synthesis, namely Unsupervised Mask (UM)-guided synthesis, to obtain both synthetic images and segmentations using limited manual segmentation labels.
no code implementations • 7 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.
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.
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.
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 • 12 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.
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.