Authors
Haridimos Kondylakis, Varvara Kalokyri, Stelios Sfakianakis, Kostas Marias, Manolis Tsiknakis, Ana Jimenez-Pastor, Eduardo Camacho-Ramos, Ignacio Blanquer, J Damian Segrelles, Sergio López-Huguet, Caroline Barelle, Magdalena Kogut-Czarkowska, Gianna Tsakou, Nikolaos Siopis, Zisis Sakellariou, Paschalis Bizopoulos, Vicky Drossou, Antonios Lalas, Konstantinos Votis, Pedro Mallol, Luis Marti-Bonmati, Leonor Cerdá Alberich, Karine Seymour, Samuel Boucher, Esther Ciarrocchi, Lauren Fromont, Jordi Rambla, Alexander Harms, Andrea Gutierrez, Martijn PA Starmans, Fred Prior, Josep Ll Gelpi, Karim Lekadir
Publication date
2023/5/8
Source
European radiology experimental
Volume
7
Issue
1
Pages
20
Publisher
Springer Vienna
Description
Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of ‘sick-care’ to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single–institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to …
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Scholar articles
H Kondylakis, V Kalokyri, S Sfakianakis, K Marias… - European radiology experimental, 2023