50 citations · 74 across the 4 of their papers we have counts for
5 papers
Adversarial learning of cancer tissue representations
Adalberto Claudio Quiros, Nicolas Coudray, Anna Yeaton +4
Deep learning based analysis of histopathology images shows promise in advancing the understanding of tumor progression, tumor micro-environment, and their underpinning biological…
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…
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…
A phylogenetic latent feature model for clonal deconvolution
Francesco Marass, Florent Mouliere, Ke Yuan +2
Tumours develop in an evolutionary process, in which the accumulation of mutations produces subpopulations of cells with distinct mutational profiles, called clones. This process l…
Reconstructing evolving signalling networks by hidden Markov nested effects models
Xin Wang, Ke Yuan, Christoph Hellmayr +2
Inferring time-varying networks is important to understand the development and evolution of interactions over time. However, the vast majority of currently used models assume direc…