39 citations · 57 across the 7 of their papers we have counts for
7 papers
EchoRisk: A Multicentre Echocardiography Dataset and Benchmark for Cardio-Oncology
Grigorios Kalliatakis, Georgia Karanasiou, Georgios Manikis +10
Therapy-induced cardiotoxicity is the leading non-oncological cause of treatment interruption in breast cancer patients, yet early, automated risk stratification from routine cardi…
TDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling
Vasileios C. Pezoulas, Nikolaos S. Tachos, Eleni Georga +3
The growing demand for privacy-preserving data sharing has positioned synthetic data generation as a critical component of responsible AI workflows. Despite notable advances in gen…
Synthetic Data Blueprint (SDB): A modular framework for the statistical, structural, and graph-based evaluation of synthetic tabular data
Vasileios C. Pezoulas, Nikolaos S. Tachos, Eleni Georga +3
In the rapidly evolving era of Artificial Intelligence (AI), synthetic data are widely used to accelerate innovation while preserving privacy and enabling broader data accessibilit…
AI Model Passport: Data and System Traceability Framework for Transparent AI in Health
Varvara Kalokyri, Nikolaos S. Tachos, Charalampos N. Kalantzopoulos +10
The increasing integration of Artificial Intelligence (AI) into health and biomedical systems necessitates robust frameworks for transparency, accountability, and ethical complianc…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging
Karim Lekadir, Richard Osuala, Catherine Gallin +13
The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI s…
A new smart-cropping pipeline for prostate segmentation using deep learning networks
Dimitrios G. Zaridis, Eugenia Mylona, Nikolaos S. Tachos +4
Prostate segmentation from magnetic resonance imaging (MRI) is a challenging task. In recent years, several network architectures have been proposed to automate this process and al…