Zobrazeno 1 - 10
of 4 475
pro vyhledávání: '"A. Papanastasiou"'
Autor:
Gao, Yijian, Marshall, Dominic, Xing, Xiaodan, Ning, Junzhi, Papanastasiou, Giorgos, Yang, Guang, Komorowski, Matthieu
Radiology reporting generative AI holds significant potential to alleviate clinical workloads and streamline medical care. However, achieving high clinical accuracy is challenging, as radiological images often feature subtle lesions and intricate str
Externí odkaz:
http://arxiv.org/abs/2411.10789
Autor:
A. Papanastasiou, A. Roubi, L. Tsitrouli, A. Antoniou, G. Vouraki, E.T. Tsapardoni, V. Drakuli, S.M. Papageorgiou, A. Pahi
Publikováno v:
European Psychiatry, Vol 65, Pp S474-S475 (2022)
Introduction Addison’s disease (AD) is a rare disorder of the adrenal glands which causes deficiency of cortisol and aldosterone. It presents with a variety of symptoms, including neuropsychiatric manifestations. We discuss the case of a patient wh
Externí odkaz:
https://doaj.org/article/c5a0a90cc54b49069aa329566ff879b7
The persistent challenge of medical image synthesis posed by the scarcity of annotated data and the need to synthesize `missing modalities' for multi-modal analysis, underscored the imperative development of effective synthesis methods. Recently, the
Externí odkaz:
http://arxiv.org/abs/2408.07196
Autor:
Ankolekar, Anshu, Boie, Sebastian, Abdollahyan, Maryam, Gadaleta, Emanuela, Hasheminasab, Seyed Alireza, Yang, Guang, Beauville, Charles, Dikaios, Nikolaos, Kastis, George Anthony, Bussmann, Michael, Khalid, Sara, Kruger, Hagen, Lambin, Philippe, Papanastasiou, Giorgos
Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data. This systemat
Externí odkaz:
http://arxiv.org/abs/2408.05249
Autor:
Xing, Xiaodan, Tang, Chunling, Murdoch, Siofra, Papanastasiou, Giorgos, Guo, Yunzhe, Xiao, Xianglu, Cross-Zamirski, Jan, Schönlieb, Carola-Bibiane, Liang, Kristina Xiao, Niu, Zhangming, Fang, Evandro Fei, Wang, Yinhai, Yang, Guang
Immunofluorescent (IF) imaging is crucial for visualizing biomarker expressions, cell morphology and assessing the effects of drug treatments on sub-cellular components. IF imaging needs extra staining process and often requiring cell fixation, there
Externí odkaz:
http://arxiv.org/abs/2407.17882
Autor:
Xing, Xiaodan, Murdoch, Siofra, Tang, Chunling, Papanastasiou, Giorgos, Cross-Zamirski, Jan, Guo, Yunzhe, Xiao, Xianglu, Schönlieb, Carola-Bibiane, Wang, Yinhai, Yang, Guang
Cell imaging assays utilizing fluorescence stains are essential for observing sub-cellular organelles and their responses to perturbations. Immunofluorescent staining process is routinely in labs, however the recent innovations in generative AI is ch
Externí odkaz:
http://arxiv.org/abs/2407.09507
Autor:
Melistas, Thomas, Spyrou, Nikos, Gkouti, Nefeli, Sanchez, Pedro, Vlontzos, Athanasios, Panagakis, Yannis, Papanastasiou, Giorgos, Tsaftaris, Sotirios A.
Generative AI has revolutionised visual content editing, empowering users to effortlessly modify images and videos. However, not all edits are equal. To perform realistic edits in domains such as natural image or medical imaging, modifications must r
Externí odkaz:
http://arxiv.org/abs/2403.20287
Deep learning (DL) has substantially enhanced natural language processing (NLP) in healthcare research. However, the increasing complexity of DL-based NLP necessitates transparent model interpretability, or at least explainability, for reliable decis
Externí odkaz:
http://arxiv.org/abs/2403.11894
Autor:
Wang, Chengjia, Papanastasiou, Giorgos
Clinical decision making from magnetic resonance imaging (MRI) combines complementary information from multiple MRI sequences (defined as 'modalities'). MRI image registration aims to geometrically 'pair' diagnoses from different modalities, time poi
Externí odkaz:
http://arxiv.org/abs/2308.01994
Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can ef
Externí odkaz:
http://arxiv.org/abs/2307.12775