1 code implementation • 31 Jan 2024 • Cagri Ozdemir, Mohammad Al Olaimat, Yashu Vashishath, Serdar Bozdag, Alzheimer's Disease Neuroimaging Initiative
Addressing these restrictions, we introduce a novel integrative neural network approach for multi-modal data networks, named Integrative Graph Convolutional Networks (IGCN).
no code implementations • 26 Sep 2023 • Brendan Williams, Dan Nguyen, Julie Vidal, Alzheimer's Disease Neuroimaging Initiative, Manojkumar Saranathan
The thalamus and its constituent nuclei are critical for a broad range of cognitive and sensorimotor processes, and implicated in many neurological and neurodegenerative conditions.
no code implementations • 1 Jul 2021 • Chenglin Yu, Dingnan Cui, Muheng Shang, Shu Zhang, Lei Guo, Junwei Han, Lei Du, Alzheimer's Disease Neuroimaging Initiative
Though deep learning models can extract the nonlinear relationship, they could not select relevant genetic factors.
no code implementations • 26 Oct 2020 • Shih-Gu Huang, Moo K. Chung, Anqi Qiu, Alzheimer's Disease Neuroimaging Initiative
This paper revisits spectral graph convolutional neural networks (graph-CNNs) given in Defferrard (2016) and develops the Laplace-Beltrami CNN (LB-CNN) by replacing the graph Laplacian with the LB operator.
no code implementations • 6 Oct 2020 • Shih-Gu Huang, Moo K. Chung, Anqi Qiu, Alzheimer's Disease Neuroimaging Initiative
Even though graph convolutional neural network (graph-CNN) has been widely used in deep learning, there is a lack of augmentation methods to generate data on graphs or surfaces.
no code implementations • 10 Aug 2020 • Hoo-chang Shin, Alvin Ihsani, Swetha Mandava, Sharath Turuvekere Sreenivas, Christopher Forster, Jiook Cha, Alzheimer's Disease Neuroimaging Initiative
Synthesizing medical images, such as PET, is a challenging task due to the fact that the intensity range is much wider and denser than those in photographs and digital renderings and are often heavily biased toward zero.
no code implementations • 10 Aug 2020 • Hoo-chang Shin, Alvin Ihsani, Ziyue Xu, Swetha Mandava, Sharath Turuvekere Sreenivas, Christopher Forster, Jiook Cha, Alzheimer's Disease Neuroimaging Initiative
This paper proposes an alternative approach to the aforementioned, where AD diagnosis is incorporated in the GAN training objective to achieve the best AD classification performance.
no code implementations • 17 Mar 2019 • Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis, José V. Manjón, D. Louis Collins, Pierrick Coupé, Alzheimer's Disease Neuroimaging Initiative
On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation.
no code implementations • 2 Jun 2017 • Murat Seckin Ayhan, Vijay Raghavan, Alzheimer's Disease Neuroimaging Initiative
Extending the basic scheme towards the Multiple Kernel Learning, we improve the efficacy of the Gaussian Process models and their interpretability in terms of the known anatomical correlates of the disease.