Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Fawaz, Abdulah"'
This paper introduces GeoMorph, a novel geometric deep-learning framework designed for image registration of cortical surfaces. The registration process consists of two main steps. First, independent feature extraction is performed on each input surf
Externí odkaz:
http://arxiv.org/abs/2311.13022
Autor:
Spitzer, Hannah, Ripart, Mathilde, Fawaz, Abdulah, Williams, Logan Z. J., project, MELD, Robinson, Emma, Iglesias, Juan Eugenio, Adler, Sophie, Wagstyl, Konrad
Focal cortical dysplasia (FCD) is a leading cause of drug-resistant focal epilepsy, which can be cured by surgery. These lesions are extremely subtle and often missed even by expert neuroradiologists. "Ground truth" manual lesion masks are therefore
Externí odkaz:
http://arxiv.org/abs/2306.01375
The neonatal cortical surface is known to be affected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes. Deep Generative models have the potential to lead to clinically
Externí odkaz:
http://arxiv.org/abs/2206.07542
The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design limitations resulting in poor modelling of long-range associations, as th
Externí odkaz:
http://arxiv.org/abs/2205.15836
Autor:
Dahan, Simon, Xu, Hao, Williams, Logan Z. J., Fawaz, Abdulah, Yang, Chunhui, Coalson, Timothy S., Williams, Michelle C., Newby, David E., Edwards, A. David, Glasser, Matthew F., Young, Alistair A., Rueckert, Daniel, Robinson, Emma C.
Recent state-of-the-art performances of Vision Transformers (ViT) in computer vision tasks demonstrate that a general-purpose architecture, which implements long-range self-attention, could replace the local feature learning operations of convolution
Externí odkaz:
http://arxiv.org/abs/2204.03408
Autor:
Dahan, Simon, Fawaz, Abdulah, Williams, Logan Z. J., Yang, Chunhui, Coalson, Timothy S., Glasser, Matthew F., Edwards, A. David, Rueckert, Daniel, Robinson, Emma C.
Publikováno v:
Proceedings of Machine Learning Research. 172 (2022) 282-303
The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design limitations resulting in poor modelling of long-range associations, as th
Externí odkaz:
http://arxiv.org/abs/2203.16414
Cortical surface registration is a fundamental tool for neuroimaging analysis that has been shown to improve the alignment of functional regions relative to volumetric approaches. Classically, image registration is performed by optimizing a complex o
Externí odkaz:
http://arxiv.org/abs/2203.12999
Autor:
Dahan, Simon, Fawaz, Abdulah, Suliman, Mohamed A., da Silva, Mariana, Williams, Logan Z. J., Rueckert, Daniel, Robinson, Emma C.
Surface meshes are a favoured domain for representing structural and functional information on the human cortex, but their complex topology and geometry pose significant challenges for deep learning analysis. While Transformers have excelled as domai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fcffb5cf7dca045e72ddf24a1cb7d622
http://arxiv.org/abs/2303.11909
http://arxiv.org/abs/2303.11909