Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Kazi, Anees"'
This study highlights the importance of conducting comprehensive model inspection as part of comparative performance analyses. Here, we investigate the effect of modelling choices on the feature learning characteristics of graph neural networks appli
Externí odkaz:
http://arxiv.org/abs/2405.01270
Autor:
Rezaei, Razieh, Dizaji, Alireza, Khakzar, Ashkan, Kazi, Anees, Navab, Nassir, Rueckert, Daniel
Neural networks are increasingly finding their way into the realm of graphs and modeling relationships between features. Concurrently graph neural network explanation approaches are being invented to uncover relationships between the nodes of the gra
Externí odkaz:
http://arxiv.org/abs/2401.00633
We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the
Externí odkaz:
http://arxiv.org/abs/2305.02199
Graph Neural Networks usually rely on the assumption that the graph topology is available to the network as well as optimal for the downstream task. Latent graph inference allows models to dynamically learn the intrinsic graph structure of problems w
Externí odkaz:
http://arxiv.org/abs/2211.16199
Pre-training has shown success in different areas of machine learning, such as Computer Vision, Natural Language Processing (NLP), and medical imaging. However, it has not been fully explored for clinical data analysis. An immense amount of clinical
Externí odkaz:
http://arxiv.org/abs/2207.10603
Autor:
Mullakaeva, Kamilia, Cosmo, Luca, Kazi, Anees, Ahmadi, Seyed-Ahmad, Navab, Nassir, Bronstein, Michael M.
Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property prediction and brain connectome analysis. Importantly, recent works have show
Externí odkaz:
http://arxiv.org/abs/2204.00323
Pre-training has shown success in different areas of machine learning, such as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging. However, it has not been fully explored for clinical data analysis. Even though an immense amo
Externí odkaz:
http://arxiv.org/abs/2203.12616
Autor:
Ghorbani, Mahsa, Bahrami, Mojtaba, Kazi, Anees, SoleymaniBaghshah, Mahdieh, Rabiee, Hamid R., Navab, Nassir
The increased amount of multi-modal medical data has opened the opportunities to simultaneously process various modalities such as imaging and non-imaging data to gain a comprehensive insight into the disease prediction domain. Recent studies using G
Externí odkaz:
http://arxiv.org/abs/2104.03597
Interpretability in Graph Convolutional Networks (GCNs) has been explored to some extent in computer vision in general, yet, in the medical domain, it requires further examination. Moreover, most of the interpretability approaches for GCNs, especiall
Externí odkaz:
http://arxiv.org/abs/2103.15587
Disease prediction is a well-known classification problem in medical applications. GCNs provide a powerful tool for analyzing the patients' features relative to each other. This can be achieved by modeling the problem as a graph node classification t
Externí odkaz:
http://arxiv.org/abs/2103.00221