225 citations · 234 across the 7 of their papers we have counts for
4 papers · 1 filter
Learning a low dimensional manifold of real cancer tissue with PathologyGAN
Adalberto Claudio Quiros, Roderick Murray-Smith, Ke Yuan
Application of deep learning in digital pathology shows promise on improving disease diagnosis and understanding. We present a deep generative model that learns to simulate high-fi…
Spatial images from temporal data
Alex Turpin, Gabriella Musarra, Valentin Kapitany +8
Traditional paradigms for imaging rely on the use of a spatial structure, either in the detector (pixels arrays) or in the illumination (patterned light). Removal of the spatial st…
PathologyGAN: Learning deep representations of cancer tissue
Adalberto Claudio Quiros, Roderick Murray-Smith, Ke Yuan
Histopathological images of tumors contain abundant information about how tumors grow and how they interact with their micro-environment. Better understanding of tissue phenotypes…
Transmission of natural scene images through a multimode fibre
Piergiorgio Caramazza, Oisín Moran, Roderick Murray-Smith +1
The optical transport of images through a multimode fibre remains an outstanding challenge with applications ranging from optical communications to neuro-imaging. State of the art…