| High-resolution mammogram synthesis using progressive generative adversarial networks D Korkinof, T Rijken, M O'Neill, J Yearsley, H Harvey, B Glocker arXiv preprint arXiv:1807.03401, 2018 | 79 | 2018 |
| A nonparametric Bayesian approach toward robot learning by demonstration SP Chatzis, D Korkinof, Y Demiris Robotics and Autonomous Systems 60 (6), 789-802, 2012 | 43 | 2012 |
| A quantum-statistical approach toward robot learning by demonstration SP Chatzis, D Korkinof, Y Demiris IEEE Transactions on Robotics 28 (6), 1371-1381, 2012 | 39 | 2012 |
| The Role of Deep Learning in Breast Screening H Harvey, E Karpati, G Khara, D Korkinof, A Ng, C Austin, T Rijken, ... Current Breast Cancer Reports, 1-6, 2019 | 25 | 2019 |
| Probabilistic abstract argumentation: an investigation with Boltzmann machines R Riveret, D Korkinof, M Draief, J Pitt Argument & Computation 6 (2), 178-218, 2015 | 17 | 2015 |
| Perceived realism of high-resolution generative adversarial network–derived synthetic mammograms D Korkinof, H Harvey, A Heindl, E Karpati, G Williams, T Rijken, ... Radiology: Artificial Intelligence 3 (2), e190181, 2020 | 14 | 2020 |
| Deep learning in breast cancer screening H Harvey, A Heindl, G Khara, D Korkinof, M O’Neill, J Yearsley, E Karpati, ... Artificial intelligence in medical imaging: opportunities, applications and …, 2019 | 14 | 2019 |
| Neuro-Symbolic Agents: Boltzmann Machines and Probabilistic Abstract Argumentation with Sub-Arguments. R Riveret, JV Pitt, D Korkinof, M Draief AAMAS, 1481-1489, 2015 | 14 | 2015 |
| Online quantum mixture regression for trajectory learning by demonstration D Korkinof, Y Demiris 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2013 | 12 | 2013 |
| Systems and methods for performing random walks on knowledge graphs M Beguerisse-Díaz, TA Hoffmann, D Korkinof US Patent App. 17/333,385, 2021 | 11 | 2021 |
| MammoGAN: High-resolution synthesis of realistic mammograms D Korkinof, A Heindl, T Rijken, H Harvey, B Glocker | 8 | 2019 |
| Multi-task and multi-kernel Gaussian process dynamical systems D Korkinof, Y Demiris Pattern Recognition 66, 190-201, 2017 | 8 | 2017 |
| The one-hidden layer non-parametric Bayesian kernel machine SP Chatzis, D Korkinof, Y Demiris 2011 IEEE 23rd International Conference on Tools with Artificial …, 2011 | 8 | 2011 |
| Second reader suggestion P Kecskemethy, T Rijken, E Karpati, M O'neill, A Heindl, JE Yearsley, ... US Patent 11,455,723, 2022 | 3 | 2022 |
| Domain adaption T Rijken, M O'neill, A Heindl, J Yearsley, D Korkinof, G Khara US Patent 11,893,659, 2024 | 2 | 2024 |
| A spatially-constrained normalized gamma process for data clustering SP Chatzis, D Korkinof, Y Demiris Artificial Intelligence Applications and Innovations: 8th IFIP WG 12.5 …, 2012 | 2 | 2012 |
| Second reader T Rijken, M O'neill, A Heindl, JE Yearsley, D Korkinof, G Khara, ... US Patent 11,410,307, 2022 | 1 | 2022 |
| A spatially-constrained normalized Gamma process prior SP Chatzis, D Korkinof, Y Demiris Expert Systems with Applications 39 (17), 13019-13025, 2012 | 1 | 2012 |
| The Kernel Pitman-Yor Process SP Chatzis, D Korkinof, Y Demiris arXiv preprint arXiv:1210.4184, 2012 | 1 | 2012 |
| Immediate workup P Kecskemethy, T Rijken, E Karpati, M O'neill, A Heindl, JE Yearsley, ... US Patent 11,488,306, 2022 | | 2022 |
Yiannis DemirisProfessor, Imperial College London; Royal Academy of Engineering Chair In Emerging TechnologiesVerified email at imperial.ac.uk
