Zobrazeno 1 - 10
of 1 876
pro vyhledávání: '"P. Suckling"'
Explainability is often critical to the acceptable implementation of artificial intelligence (AI). Nowhere is this more important than healthcare where decision-making directly impacts patients and trust in AI systems is essential. This trust is ofte
Externí odkaz:
http://arxiv.org/abs/2406.00216
Autor:
Mamalakis, Michail, de Vareilles, Héloïse, Wu, Shun-Chin Jim, Agartz, Ingrid, Mørch-Johnsen, Lynn Egeland, Garrison, Jane, Simons, Jon, Lio, Pietro, Suckling, John, Murray, Graham
In the last decade, computer vision has witnessed the establishment of various training and learning approaches. Techniques like adversarial learning, contrastive learning, diffusion denoising learning, and ordinary reconstruction learning have becom
Externí odkaz:
http://arxiv.org/abs/2405.19204
Autor:
Mamalakis, Michail, Mamalakis, Antonios, Agartz, Ingrid, Mørch-Johnsen, Lynn Egeland, Murray, Graham, Suckling, John, Lio, Pietro
The accelerated progress of artificial intelligence (AI) has popularized deep learning models across domains, yet their inherent opacity poses challenges, notably in critical fields like healthcare, medicine and the geosciences. Explainable AI (XAI)
Externí odkaz:
http://arxiv.org/abs/2405.10008
Autor:
Gorriz, Juan M, Ramirez, J., Segovia, F., Martinez-Murcia, F. J., Jiménez-Mesa, C., Suckling, J.
Regression analysis is a central topic in statistical modeling, aiming to estimate the relationships between a dependent variable, commonly referred to as the response variable, and one or more independent variables, i.e., explanatory variables. Line
Externí odkaz:
http://arxiv.org/abs/2402.15213
As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference. K-fold cross-validation (CV) is the most common approach to ascertaining the likelihood that a machine learning outcome
Externí odkaz:
http://arxiv.org/abs/2401.16407
Autor:
Mamalakis, Michail, de Vareilles, Heloise, AI-Manea, Atheer, Mitchell, Samantha C., Arartz, Ingrid, Morch-Johnsen, Lynn Egeland, Garrison, Jane, Simons, Jon, Lio, Pietro, Suckling, John, Murray, Graham
The significant features identified in a representative subset of the dataset during the learning process of an artificial intelligence model are referred to as a 'global' explanation. Three-dimensional (3D) global explanations are crucial in neuroim
Externí odkaz:
http://arxiv.org/abs/2309.00903
Autor:
Maite Aznarez-Sanado, Rafael Romero-Garcia, Chao Li, Rob C. Morris, Stephen J. Price, Thomas Manly, Thomas Santarius, Yaara Erez, Michael G. Hart, John Suckling
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Brain tumour microstructure is potentially predictive of changes following treatment to cognitive functions subserved by the functional networks in which they are embedded. To test this hypothesis, intra-tumoural microstructure was quantifie
Externí odkaz:
https://doaj.org/article/0184f54623fe44e6ac4516b86b08c769
Publikováno v:
Volume 510, 21 October 2022, Pages 159-171
There remains an open question about the usefulness and the interpretation of Machine learning (MLE) approaches for discrimination of spatial patterns of brain images between samples or activation states. In the last few decades, these approaches hav
Externí odkaz:
http://arxiv.org/abs/2202.04397
Autor:
Xiao-He Hou, John Suckling, Xue-Ning Shen, Yong Liu, Chuan-Tao Zuo, Yu-Yuan Huang, Hong-Qi Li, Hui-Fu Wang, Chen-Chen Tan, Mei Cui, Qiang Dong, Lan Tan, Jin-Tai Yu, Alzheimer’s Disease Neuroimaging Initiative
Publikováno v:
Journal of Translational Medicine, Vol 21, Iss 1, Pp 1-12 (2023)
Abstract Background Early prevention of Alzheimer’s disease (AD) is a feasible way to delay AD onset and progression. Information on AD prediction at the individual patient level will be useful in AD prevention. In this study, we aim to develop ris
Externí odkaz:
https://doaj.org/article/29e263d09a744ddca09eb5252a873c92
Autor:
Christopher Riley, Usama Ammar, Aisha Alsfouk, Nahoum G. Anthony, Jessica Baiget, Giacomo Berretta, David Breen, Judith Huggan, Christopher Lawson, Kathryn McIntosh, Robin Plevin, Colin J. Suckling, Louise C. Young, Andrew Paul, Simon P. Mackay
Publikováno v:
Molecules, Vol 29, Iss 15, p 3515 (2024)
The inhibitory-kappaB kinases (IKKs) IKKα and IKKβ play central roles in regulating the non-canonical and canonical NF-κB signalling pathways. Whilst the proteins that transduce the signals of each pathway have been extensively characterised, the
Externí odkaz:
https://doaj.org/article/e3e2e03e3b834ed6981a0d5113267e5c