Zobrazeno 1 - 10
of 154
pro vyhledávání: '"Robinson, Emma C."'
Existing learning-based cortical surface reconstruction approaches heavily rely on the supervision of pseudo ground truth (pGT) cortical surfaces for training. Such pGT surfaces are generated by traditional neuroimage processing pipelines, which are
Externí odkaz:
http://arxiv.org/abs/2406.12650
Autor:
Munroe, Lindsay, da Silva, Mariana, Heidari, Faezeh, Grigorescu, Irina, Dahan, Simon, Robinson, Emma C., Deprez, Maria, So, Po-Wah
Clinical adoption of deep learning models has been hindered, in part, because the black-box nature of neural networks leads to concerns regarding their trustworthiness and reliability. These concerns are particularly relevant in the field of neuroima
Externí odkaz:
http://arxiv.org/abs/2406.17792
Autor:
Ma, Qiang, Liang, Kaili, Li, Liu, Masui, Saga, Guo, Yourong, Nosarti, Chiara, Robinson, Emma C., Kainz, Bernhard, Rueckert, Daniel
The Developing Human Connectome Project (dHCP) aims to explore developmental patterns of the human brain during the perinatal period. An automated processing pipeline has been developed to extract high-quality cortical surfaces from structural brain
Externí odkaz:
http://arxiv.org/abs/2405.08783
Cortical surface analysis has gained increased prominence, given its potential implications for neurological and developmental disorders. Traditional vision diffusion models, while effective in generating natural images, present limitations in captur
Externí odkaz:
http://arxiv.org/abs/2402.04753
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
The development of robust and generalisable models for encoding the spatio-temporal dynamics of human brain activity is crucial for advancing neuroscientific discoveries. However, significant individual variation in the organisation of the human cere
Externí odkaz:
http://arxiv.org/abs/2308.05474
Autor:
Ma, Qiang, Li, Liu, Kyriakopoulou, Vanessa, Hajnal, Joseph, Robinson, Emma C., Kainz, Bernhard, Rueckert, Daniel
Cortical surface reconstruction plays a fundamental role in modeling the rapid brain development during the perinatal period. In this work, we propose Conditional Temporal Attention Network (CoTAN), a fast end-to-end framework for diffeomorphic neona
Externí odkaz:
http://arxiv.org/abs/2307.11870
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:
http://arxiv.org/abs/2303.11909
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