Publications & Patents

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2022

P. Kecskemethy, T. Rijken, E. Karpati, M. O'neill, A. Heindl, J. Yearsley, D. Korkinof, G. Khara

Immediate workup

US Patent 11,488,306 , Nov 2022

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P. Kecskemethy, T. Rijken, E. Karpati, M. O'Neill, A. Heindl, J. Yearsley, D. Korkinof, G. Khara

Second Reader Suggestion

US Patent 11,455,723 , Oct 2022

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T. Rijken, M. O'neill, A. Heindl, J. Yearsley, D. Korkinof, G. Khara, P. Kecskemethy, E. Karpati

Second reader

US Patent 11,410,307 , Aug 2022

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T. Rijken, M. O'neill, A. Heindl, J. Yearsley, D. Korkinof, G. Khara

Domain adaption

US Patent App. 17/309,275 , Jan 2022

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2021

M. Beguerisse-Diaz, T. Hoffmann, D. Korkinof

Systems and methods for performing random walks on knowledge graphs

US Patent App. 17/333,385 , Dec 2021

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M. Beguerisse-Diaz, D. Korkinof, T. Hoffmann

Thematic recommendations on knowledge graphs using multilayer networks

arXiv: 2105.05733 , May 2021

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2020

D. Korkinof, H. Harvey, A. Heindl, E. Karpati, G. Williams, T. Rijken, P. Kecskemethy, B. Glocker

Perceived Realism of High-Resolution Generative Adversarial Network–derived Synthetic Mammograms

Radiology: Artificial Intelligence , Dec 2020

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2019

D. Korkinof, A. Heindl, T. Rijken, H. Harvey, B. Glocker

MammoGAN: High-Resolution Synthesis of Realistic Mammograms

Medical Imaging with Deep Learning (MIDL) Workshops , May 2019

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H. Harvey, E. Karpati, G. Khara, D. Korkinof, A. Ng, C. Austin, T. Rijken, P. Kecskemethy

The Role of Deep Learning in Breast Screening

Current Breast Cancer Reports, pp 1-6 , Feb 2019

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H. Harvey, A. Heindl, G. Khara, D. Korkinof, M. O’Neill, J. Yearsley, E. Karpati, T. Rijken, P. Kecskemethy, G. Forrai

Deep Learning in Breast Cancer Screening

Deep Learning in Breast Cancer Screening, pp 187-215 , Jan 2019

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2018

D. Korkinof, T. Rijken, M. O'Neil, J. Yearsley, H. Harvey and B. Glocker

High-resolution mammogram synthesis using progressive generative adversarial networks

arXiv: 1807.03401 , Jul 2018

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2017

D. Korkinof and Y. Demiris

Multi-task and multi-kernel Gaussian process dynamical systems

Pattern Recognition, vol. 66, pp. 190-201 , Jan 2017

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2015

R. Riveret, D. Korkinof, M. Draief and J. Pitt

Probabilistic abstract argumentation: An Investigation with Boltzmann machines

Journal of Argument and Computation, 6(2), pp.178-218 , Nov 2015

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R. Riveret, J. Pitt, D. Korkinof and M. Draief

Epistemic Neuro-Symoblic Agents: Boltzmann Machines and Abstract Probabilistic Argumentation with Supports

International Conference on Autonomous Agents and Multi-agent Systems (AAMAS) , Jun 2015

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2013

D. Korkinof and Y. Demiris

Online Quantum Mixture Regression for Trajectory Learning by Demonstration

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Nov 2013

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2012

S. Chatzis, D. Korkinof and Y. Demiris

A Quantum-Statistical Approach Towards Robot Learning by Demonstration

IEEE Transactions on Robotics, vol. 28, no. 6, pp. 1371–1381 , Nov 2012

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S. Chatzis, D. Korkinof and Y. Demiris

A Spatially-Constrained Normalised Gamma Process for Data Clustering

International Conference on Artificial Intelligence Applications and Innovations (AIAI) , Sep 2012

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S. Chatzis, D. Korkinof and Y. Demiris

A Spatially-Constrained Normalized Gamma Process Prior

Expert Systems with Applications, vol. 39, no. 17, pp. 13019–13025 , Apr 2012

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S. Chatzis, D. Korkinof and Y. Demiris

A Nonparametric Bayesian Approach Towards Robot Learning by Demonstration

Journal of Robotics and Autonomous Systems, vol. 60, no. 6, pp. 789–802 , Feb 2012

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2011

S. Chatzis, D. Korkinof and Y. Demiris

The One-Hidden Layer Non-parametric Bayesian Kernel Machine

IEEE International Conference on Tools with Artificial Intelligence (ICTAI) , Nov 2011

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