Search Results for author: Alzheimer's Disease Neuroimaging Initiative

Found 9 papers, 1 papers with code

IGCN: Integrative Graph Convolutional Networks for Multi-modal Data

1 code implementation31 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).

Node Classification

Thalamic nuclei segmentation from T$_1$-weighted MRI: unifying and benchmarking state-of-the-art methods with young and old cohorts

no code implementations26 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.

Benchmarking Segmentation +1

Revisiting convolutional neural network on graphs with polynomial approximations of Laplace-Beltrami spectral filtering

no code implementations26 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.

Classification General Classification

Fast Mesh Data Augmentation via Chebyshev Polynomial of Spectral filtering

no code implementations6 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.

Data Augmentation

GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis

no code implementations10 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.

Sentence

GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI

no code implementations10 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.

Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data

no code implementations2 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.

Gaussian Processes General Classification +1

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