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pro vyhledávání: '"Cole, James"'
The potential future democratization of spaceflight reveals a need for design of experiences that extend beyond our current conceptualization of spaceflight. Research on career astronauts indicates that transformative experiences occur during spacefl
Externí odkaz:
http://arxiv.org/abs/2408.00085
Autor:
Ijishakin, Ayodeji, Hadjasavilou, Adamos, Abdulaal, Ahmed, Montana-Brown, Nina, Townend, Florence, Spinelli, Edoardo, Fillipi, Massimo, Agosta, Federica, Cole, James, Malaspina, Andrea
Predicting survival in Amyotrophic Lateral Sclerosis (ALS) is a challenging task. Magnetic resonance imaging (MRI) data provide in vivo insight into brain health, but the low prevalence of the condition and resultant data scarcity limit training set
Externí odkaz:
http://arxiv.org/abs/2407.14191
Autor:
Agarwal, Siddharth, Wood, David A., Grzeda, Mariusz, Suresh, Chandhini, Din, Munaib, Cole, James, Modat, Marc, Booth, Thomas C
Purpose: Most studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently well-validated, leading to poor generalisability to real-world
Externí odkaz:
http://arxiv.org/abs/2405.05658
Autor:
Wood, David A., Guilhem, Emily, Kafiabadi, Sina, Busaidi, Ayisha Al, Dissanayake, Kishan, Hammam, Ahmed, Mansoor, Nina, Townend, Matthew, Agarwal, Siddharth, Wei, Yiran, Mazumder, Asif, Barker, Gareth J., Sasieni, Peter, Ourselin, Sebastien, Cole, James H., Booth, Thomas C.
Artificial neural networks trained on large, expert-labelled datasets are considered state-of-the-art for a range of medical image recognition tasks. However, categorically labelled datasets are time-consuming to generate and constrain classification
Externí odkaz:
http://arxiv.org/abs/2405.02782
Autor:
Ijishakin, Ayodeji, Martin, Sophie, Townend, Florence, Agosta, Federica, Spinelli, Edoardo Gioele, Basaia, Silvia, Schito, Paride, Falzone, Yuri, Filippi, Massimo, Cole, James, Malaspina, Andrea
Publikováno v:
Deep Generative Models for Health Workshop, NeurIPS 2023
Brain age prediction models have succeeded in predicting clinical outcomes in neurodegenerative diseases, but can struggle with tasks involving faster progressing diseases and low quality data. To enhance their performance, we employ a semi-supervise
Externí odkaz:
http://arxiv.org/abs/2402.09137
Autor:
Parker, Christopher S., Schroder, Anna, Epstein, Sean C., Cole, James, Alexander, Daniel C., Zhang, Hui
Purpose: Previous quantitative MR imaging studies using self-supervised deep learning have reported biased parameter estimates at low SNR. Such systematic errors arise from the choice of Mean Squared Error (MSE) loss function for network training, wh
Externí odkaz:
http://arxiv.org/abs/2307.07072
Publikováno v:
ICML (2023), 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH)
In visual object classification, humans often justify their choices by comparing objects to prototypical examples within that class. We may therefore increase the interpretability of deep learning models by imbuing them with a similar style of reason
Externí odkaz:
http://arxiv.org/abs/2306.03022
Autor:
Popescu, Sebastian G., Sharp, David J., Cole, James H., Kamnitsas, Konstantinos, Glocker, Ben
Machine learning models deployed on medical imaging tasks must be equipped with out-of-distribution detection capabilities in order to avoid erroneous predictions. It is unsure whether out-of-distribution detection models reliant on deep neural netwo
Externí odkaz:
http://arxiv.org/abs/2206.13346