Zobrazeno 1 - 10
of 4 453
pro vyhledávání: '"Dinsdale, A."'
Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many factors, including the lack of automated image analysis tools, especially
Externí odkaz:
http://arxiv.org/abs/2410.06161
Autor:
Ramesh, Jayroop, Dinsdale, Nicola K, Consortium, the INTERGROWTH-21st, Yeung, Pak-Hei, Namburete, Ana IL
Accurately localizing two-dimensional (2D) ultrasound (US) fetal brain images in the 3D brain, using minimal computational resources, is an important task for automated US analysis of fetal growth and development. We propose an uncertainty-aware deep
Externí odkaz:
http://arxiv.org/abs/2405.13235
The lack of explainability of deep learning models limits the adoption of such models in clinical practice. Prototype-based models can provide inherent explainable predictions, but these have predominantly been designed for classification tasks, desp
Externí odkaz:
http://arxiv.org/abs/2306.09858
To represent the biological variability of clinical neuroimaging populations, it is vital to be able to combine data across scanners and studies. However, different MRI scanners produce images with different characteristics, resulting in a domain shi
Externí odkaz:
http://arxiv.org/abs/2303.15965
Autor:
Esten H. Leonardsen, Karin Persson, Edvard Grødem, Nicola Dinsdale, Till Schellhorn, James M. Roe, Didac Vidal-Piñeiro, Øystein Sørensen, Tobias Kaufmann, Eric Westman, Andre Marquand, Geir Selbæk, Ole A. Andreassen, Thomas Wolfers, Lars T. Westlye, Yunpeng Wang
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-14 (2024)
Abstract Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited
Externí odkaz:
https://doaj.org/article/eb18586dd26d4b43a46d7dbb90ca469c
Autor:
Ria L. Dinsdale, Andrea L. Meredith
Publikováno v:
Channels, Vol 18, Iss 1 (2024)
Variants in KCNMA1, encoding the voltage- and calcium-activated K+ (BK) channel, are associated with human neurological disease. The effects of gain-of-function (GOF) and loss-of-function (LOF) variants have been predominantly studied on BK channel c
Externí odkaz:
https://doaj.org/article/cbec9ee4309246189d702c30837c6248
The ability to combine data across scanners and studies is vital for neuroimaging, to increase both statistical power and the representation of biological variability. However, combining datasets across sites leads to two challenges: first, an increa
Externí odkaz:
http://arxiv.org/abs/2205.15970
Publikováno v:
In Journal of Chromatography B 1 November 2024 1248
Publikováno v:
In Musculoskeletal Science and Practice October 2024 73
Publikováno v:
In Medical Image Analysis October 2024 97