Zobrazeno 1 - 10
of 2 024
pro vyhledávání: '"Lotery A"'
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
Publikováno v:
Biologics: Targets & Therapy, Vol Volume 14, Pp 83-94 (2020)
Engin Akyol, Andrew Lotery Clinical Neurosciences Research Group, Faculty of Medicine, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UKCorrespondence: Andrew LoteryClinical Neurosciences Research Group, Faculty of Med
Externí odkaz:
https://doaj.org/article/662d7e46cea145a9a309137ec7852940
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:
Patel PJ, Devonport H, Sivaprasad S, Ross AH, Walters G, Gale RP, Lotery AJ, Mahmood S, Talks JS, Napier J
Publikováno v:
Clinical Ophthalmology, Vol Volume 11, Pp 1957-1966 (2017)
Praveen J Patel,1 Helen Devonport,2 Sobha Sivaprasad,1 Adam H Ross,3 Gavin Walters,4 Richard P Gale,5 Andrew J Lotery,6 Sajjad Mahmood,7 James S Talks,8 Jackie Napier9 1National Institute for Health Research Biomedical Research Centre at Moorfields E
Externí odkaz:
https://doaj.org/article/1630e390b8cb4fc2b403a25fc6033d82
Publikováno v:
Clinical Ophthalmology, Vol 2016, Iss Issue 1, Pp 87-96 (2016)
Philip Hykin,1 Usha Chakravarthy,2 Andrew Lotery,3 Martin McKibbin,4 Jackie Napier,5 Sobha Sivaprasad1,6 On behalf of the AURA Study Group 1National Institute for Health Research Biomedical Research Centre in Ophthalmology, Moorfields Eye Hospital, L
Externí odkaz:
https://doaj.org/article/fd84137aba1148d3ae55e7a656c76a1c
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