Zobrazeno 1 - 10
of 2 036
pro vyhledávání: '"A Lotery"'
Autor:
Chakravarty, Arunava, Emre, Taha, Lachinov, Dmitrii, Rivail, Antoine, Scholl, Hendrik, Fritsche, Lars, Sivaprasad, Sobha, Rueckert, Daniel, Lotery, Andrew, Schmidt-Erfurth, Ursula, Bogunović, Hrvoje
Predicting future disease progression risk from medical images is challenging due to patient heterogeneity, and subtle or unknown imaging biomarkers. Moreover, deep learning (DL) methods for survival analysis are susceptible to image domain shifts ac
Externí odkaz:
http://arxiv.org/abs/2409.20195
Autor:
Holland, Robbie, Taylor, Thomas R. P., Holmes, Christopher, Riedl, Sophie, Mai, Julia, Patsiamanidi, Maria, Mitsopoulou, Dimitra, Hager, Paul, Müller, Philip, Scholl, Hendrik P. N., Bogunović, Hrvoje, Schmidt-Erfurth, Ursula, Rueckert, Daniel, Sivaprasad, Sobha, Lotery, Andrew J., Menten, Martin J.
Clinicians spend a significant amount of time reviewing medical images and transcribing their findings regarding patient diagnosis, referral and treatment in text form. Vision-language models (VLMs), which automatically interpret images and summarize
Externí odkaz:
http://arxiv.org/abs/2407.08410
Autor:
Holland, Robbie, Kaye, Rebecca, Hagag, Ahmed M., Leingang, Oliver, Taylor, Thomas R. P., Bogunović, Hrvoje, Schmidt-Erfurth, Ursula, Scholl, Hendrik P. N., Rueckert, Daniel, Lotery, Andrew J., Sivaprasad, Sobha, Menten, Martin J.
Diseases are currently managed by grading systems, where patients are stratified by grading systems into stages that indicate patient risk and guide clinical management. However, these broad categories typically lack prognostic value, and proposals f
Externí odkaz:
http://arxiv.org/abs/2405.09549
Autor:
Shen, Chengzhi, Menten, Martin J., Bogunović, Hrvoje, Schmidt-Erfurth, Ursula, Scholl, Hendrik, Sivaprasad, Sobha, Lotery, Andrew, Rueckert, Daniel, Hager, Paul, Holland, Robbie
Analyzing temporal developments is crucial for the accurate prognosis of many medical conditions. Temporal changes that occur over short time scales are key to assessing the health of physiological functions, such as the cardiac cycle. Moreover, trac
Externí odkaz:
http://arxiv.org/abs/2403.07513
Autor:
Emre, Taha, Chakravarty, Arunava, Rivail, Antoine, Lachinov, Dmitrii, Leingang, Oliver, Riedl, Sophie, Mai, Julia, Scholl, Hendrik P. N., Sivaprasad, Sobha, Rueckert, Daniel, Lotery, Andrew, Schmidt-Erfurth, Ursula, Bogunović, Hrvoje
Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent family of SSL that extract similar representations of two augmented views o
Externí odkaz:
http://arxiv.org/abs/2312.16980
Autor:
Emre, Taha, Oghbaie, Marzieh, Chakravarty, Arunava, Rivail, Antoine, Riedl, Sophie, Mai, Julia, Scholl, Hendrik P. N., Sivaprasad, Sobha, Rueckert, Daniel, Lotery, Andrew, Schmidt-Erfurth, Ursula, Bogunović, Hrvoje
In the field of medical imaging, 3D deep learning models play a crucial role in building powerful predictive models of disease progression. However, the size of these models presents significant challenges, both in terms of computational resources an
Externí odkaz:
http://arxiv.org/abs/2307.13865
Autor:
Chakravarty, Arunava, Emre, Taha, Leingang, Oliver, Riedl, Sophie, Mai, Julia, Scholl, Hendrik P. N., Sivaprasad, Sobha, Rueckert, Daniel, Lotery, Andrew, Schmidt-Erfurth, Ursula, Bogunović, Hrvoje
The lack of reliable biomarkers makes predicting the conversion from intermediate to neovascular age-related macular degeneration (iAMD, nAMD) a challenging task. We develop a Deep Learning (DL) model to predict the future risk of conversion of an ey
Externí odkaz:
http://arxiv.org/abs/2304.08439
Autor:
Holland, Robbie, Leingang, Oliver, Holmes, Christopher, Anders, Philipp, Kaye, Rebecca, Riedl, Sophie, Paetzold, Johannes C., Ezhov, Ivan, Bogunović, Hrvoje, Schmidt-Erfurth, Ursula, Fritsche, Lars, Scholl, Hendrik P. N., Sivaprasad, Sobha, Lotery, Andrew J., Rueckert, Daniel, Menten, Martin J.
Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into broad categories and are unable to predict future disease progression
Externí odkaz:
http://arxiv.org/abs/2301.04525
Autor:
Holland, Robbie, Leingang, Oliver, Bogunović, Hrvoje, Riedl, Sophie, Fritsche, Lars, Prevost, Toby, Scholl, Hendrik P. N., Schmidt-Erfurth, Ursula, Sivaprasad, Sobha, Lotery, Andrew J., Rueckert, Daniel, Menten, Martin J.
Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-
Externí odkaz:
http://arxiv.org/abs/2208.02529
Autor:
Ruth E Hogg, Robin Wickens, Sean O’Connor, Eleanor Gidman, Elizabeth Ward, Charlene Treanor, Tunde Peto, Ben Burton, Paul Knox, Andrew J Lotery, Sobha Sivaprasad, Michael Donnelly, Chris A Rogers, Barnaby C Reeves
Publikováno v:
Health Technology Assessment, Vol 28, Iss 32 (2024)
Background Most neovascular age-related macular degeneration treatments involve long-term follow-up of disease activity. Home monitoring would reduce the burden on patients and those they depend on for transport, and release clinic appointments for o
Externí odkaz:
https://doaj.org/article/89899ac56210458fb80dba205e94234c
