Secure, privacy-preserving and federated machine learning in medical imaging
2020 · Nature Machine Intelligence · 1,188 citations
https://doi.org/10.1038/s42256-020-0186-1
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Georgios Kaissis
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Georgios Kaissis is an academic researcher from Technical University of Munich. The author has contributed to research in topics: Privacy-Preserving Technologies in Data & Radiomics and Machine Learning in Medical Imaging & COVID-19 diagnosis using AI. The author has an h-index of 30, co-authored 206 publications.
ORCID: 0000-0001-8382-8062Papers by this author
worksThe Liver Tumor Segmentation Benchmark (LiTS)
2022 · Medical Image Analysis · 1,082 citations
https://doi.org/10.1016/j.media.2022.102680
Evaluation and mitigation of the limitations of large language models in clinical decision-making
2024 · Nature Medicine · 453 citations
https://doi.org/10.1038/s41591-024-03097-1
End-to-end privacy preserving deep learning on multi-institutional medical imaging
2021 · Nature Machine Intelligence · 443 citations
https://doi.org/10.1038/s42256-021-00337-8
FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare
2025 · BMJ · 237 citations
https://doi.org/10.1136/bmj-2024-081554
Interactive and Explainable Region-guided Radiology Report Generation
2023 · 145 citations
https://doi.org/10.1109/cvpr52729.2023.00718
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