1 code implementation • 4 Jul 2021 • Spyridon Thermos, Xiao Liu, Alison O'Neil, Sotirios A. Tsaftaris
Motivated by the ability to disentangle images into spatial anatomy (tensor) factors and accompanying imaging (vector) representations, we propose a framework termed "disentangled anatomy arithmetic", in which a generative model learns to combine anatomical factors of different input images such that when they are re-entangled with the desired imaging modality (e. g. MRI), plausible new cardiac images are created with the target characteristics.
2 code implementations • 24 Jun 2021 • Xiao Liu, Spyridon Thermos, Alison O'Neil, Sotirios A. Tsaftaris
We explicitly model the representations related to domain shifts.
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.