Zobrazeno 1 - 10
of 417
pro vyhledávání: '"Haridimos, Kondylakis"'
Autor:
Haridimos Kondylakis, Rocio Catalan, Sara Martinez Alabart, Caroline Barelle, Paschalis Bizopoulos, Maciej Bobowicz, Jonathan Bona, Dimitrios I. Fotiadis, Teresa Garcia, Ignacio Gomez, Ana Jimenez-Pastor, Giannis Karatzanis, Karim Lekadir, Magdalena Kogut-Czarkowska, Antonios Lalas, Kostas Marias, Luis Marti-Bonmati, Jose Munuera, Katerina Nikiforaki, Manon Pelissier, Fred Prior, Michael Rutherford, Laure Saint-Aubert, Zisis Sakellariou, Karine Seymour, Thomas Trouillard, Konstantinos Votis, Manolis Tsiknakis
Publikováno v:
Insights into Imaging, Vol 15, Iss 1, Pp 1-8 (2024)
Abstract Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive “sick-care” approach to a proactive focus on healthcare and prevention. The successful development of A
Externí odkaz:
https://doaj.org/article/f7384a2ef8654d549797dd949a48e4b4
Autor:
Vassilis Kilintzis, Varvara Kalokyri, Haridimos Kondylakis, Smriti Joshi, Katerina Nikiforaki, Oliver Díaz, Karim Lekadir, Manolis Tsiknakis, Kostas Marias
Publikováno v:
European Radiology Experimental, Vol 8, Iss 1, Pp 1-12 (2024)
Abstract Background Developing trustworthy artificial intelligence (AI) models for clinical applications requires access to clinical and imaging data cohorts. Reusing of publicly available datasets has the potential to fill this gap. Specifically in
Externí odkaz:
https://doaj.org/article/39fe1ae5fca14d0083fddafc7d264d39
Autor:
Giannis Vassiliou, Georgia Eirini Trouli, Georgia Troullinou, Nikolaos Spyridakis, George Bitzarakis, Fotini Droumalia, Antonis Karagiannakis, Georgia Skouteli, Nikolaos Oikonomou, Dimitra Deka, Emmanouil Makaronas, Georgios Pronoitis, Konstantinos Alexandris, Stamatios Kostopoulos, Yiannis Kazantzakis, Nikolaos Vlassis, Eleftheria Sfinarolaki, Vardis Daskalakis, Iakovos Giannakos, Argyro Stamatoukou, Nikolaos Papadakis, Haridimos Kondylakis
Publikováno v:
Applied Sciences, Vol 14, Iss 17, p 7640 (2024)
The exponential growth of Knowledge Graphs necessitates effective and efficient methods for their exploration and understanding. Frequently Asked Questions (FAQ) is a service that typically presents a list of questions and answers related to a specif
Externí odkaz:
https://doaj.org/article/26c7c41990164957a3c15914a1e8f674
Autor:
Paula Poikonen-Saksela, Evangelos Karademas, Leena Vehmanen, Meri Utriainen, Haridimos Kondylakis, Konstadina Kourou, Georgios C. Manikis, Eleni Kolokotroni, Panagiotis Argyropaidas, Berta Sousa, Ruth Pat Horenczyk, Ketti Mazzocco, Johanna Mattson
Publikováno v:
The Breast Journal, Vol 2024 (2024)
Background. Despite excellent prognosis of early breast cancer, the patients face problems related to decreased quality of life and mental health. There is a need for easily available interventions targeting modifiable factors related to these proble
Externí odkaz:
https://doaj.org/article/420d231460c040408477e5898700800c
Autor:
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 P. A. Starmans, Fred Prior, Josep Ll. Gelpi, Karim Lekadir
Publikováno v:
European Radiology Experimental, Vol 7, Iss 1, Pp 1-13 (2023)
Abstract 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, comple
Externí odkaz:
https://doaj.org/article/a9710911be714bd29067ed9733586a3c
Autor:
Konstantina Kourou, Georgios Manikis, Eugenia Mylona, Paula Poikonen-Saksela, Ketti Mazzocco, Ruth Pat-Horenczyk, Berta Sousa, Albino J. Oliveira-Maia, Johanna Mattson, Ilan Roziner, Greta Pettini, Haridimos Kondylakis, Kostas Marias, Mikko Nuutinen, Evangelos Karademas, Panagiotis Simos, Dimitrios I. Fotiadis
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract Identifying individual patient characteristics that contribute to long-term mental health deterioration following diagnosis of breast cancer (BC) is critical in clinical practice. The present study employed a supervised machine learning pipe
Externí odkaz:
https://doaj.org/article/ddd695d1a3c9476eb3eb09ac3e014396
Publikováno v:
Information, Vol 15, Iss 4, p 238 (2024)
The increase in the size and complexity of large knowledge graphs now available online has resulted in the emergence of many approaches focusing on enabling the quick exploration of the content of those data sources. Structural non-quotient semantic
Externí odkaz:
https://doaj.org/article/a16f7749e38b4a119806ced270b07ec1
Autor:
Chaim David, Haridimos Kondylakis
Publikováno v:
Information, Vol 15, Iss 1, p 56 (2024)
Over time, the renowned Kyoto Encyclopedia of Genes and Genomes (KEGG) has grown to become one of the most comprehensive online databases for biological procedures. The majority of the data are stored in the form of pathways, which are graphs that de
Externí odkaz:
https://doaj.org/article/9cb39c302ff6462fb8063585af93ac79
Autor:
Haridimos Kondylakis, Esther Ciarrocchi, Leonor Cerda-Alberich, Ioanna Chouvarda, Lauren A. Fromont, Jose Manuel Garcia-Aznar, Varvara Kalokyri, Alexandra Kosvyra, Dawn Walker, Guang Yang, Emanuele Neri, the AI4HealthImaging Working Group on metadata models
Publikováno v:
European Radiology Experimental, Vol 6, Iss 1, Pp 1-15 (2022)
Abstract A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has
Externí odkaz:
https://doaj.org/article/9260b95785014647b1b1eb29e757bf4c
Autor:
Georgios C Manikis, Nicholas J Simos, Konstantina Kourou, Haridimos Kondylakis, Paula Poikonen-Saksela, Ketti Mazzocco, Ruth Pat-Horenczyk, Berta Sousa, Albino J Oliveira-Maia, Johanna Mattson, Ilan Roziner, Chiara Marzorati, Kostas Marias, Mikko Nuutinen, Evangelos Karademas, Dimitrios Fotiadis
Publikováno v:
Journal of Medical Internet Research, Vol 25, p e43838 (2023)
BackgroundHealth professionals are often faced with the need to identify women at risk of manifesting poor psychological resilience following the diagnosis and treatment of breast cancer. Machine learning algorithms are increasingly used to support c
Externí odkaz:
https://doaj.org/article/021ace5958554eb6948492eaeea133b6