12 citations · 14 across the 3 of their papers we have counts for
6 papers
Controllable cardiac synthesis via disentangled anatomy arithmetic
Spyridon Thermos, Xiao Liu, Alison O'Neil +1
Acquiring annotated data at scale with rare diseases or conditions remains a challenge. It would be extremely useful to have a method that controllably synthesizes images that can…
Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation
Xiao Liu, Spyridon Thermos, Alison O'Neil +1
Generalising deep models to new data from new centres (termed here domains) remains a challenge. This is largely attributed to shifts in data statistics (domain shifts) between sou…
Disentangled Representations for Domain-generalized Cardiac Segmentation
Xiao Liu, Spyridon Thermos, Agisilaos Chartsias +2
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. S…
A Deep Learning Approach to Object Affordance Segmentation
Spyridon Thermos, Petros Daras, Gerasimos Potamianos
Learning to understand and infer object functionalities is an important step towards robust visual intelligence. Significant research efforts have recently focused on segmenting th…
Deep Soft Procrustes for Markerless Volumetric Sensor Alignment
Vladimiros Sterzentsenko, Alexandros Doumanoglou, Spyridon Thermos +3
With the advent of consumer grade depth sensors, low-cost volumetric capture systems are easier to deploy. Their wider adoption though depends on their usability and by extension o…
Self-Supervised Deep Depth Denoising
Vladimiros Sterzentsenko, Leonidas Saroglou, Anargyros Chatzitofis +5
Depth perception is considered an invaluable source of information for various vision tasks. However, depth maps acquired using consumer-level sensors still suffer from non-negligi…