Graph convolutional neural networks for Alzheimer's disease classification
Graph convolutional neural networks (GCNNs) aim to extend the data representation and
classification capabilities of convolutional neural networks, which are highly effective for …
classification capabilities of convolutional neural networks, which are highly effective for …
PET image super-resolution using generative adversarial networks
The intrinsically low spatial resolution of positron emission tomography (PET) leads to
image quality degradation and inaccurate image-based quantitation. Recently developed …
image quality degradation and inaccurate image-based quantitation. Recently developed …
Super-resolution PET imaging using convolutional neural networks
Positron emission tomography (PET) suffers from severe resolution limitations which reduce
its quantitative accuracy. In this article, we present a super-resolution (SR) imaging …
its quantitative accuracy. In this article, we present a super-resolution (SR) imaging …
[HTML][HTML] AI-Driven sleep staging from actigraphy and heart rate
TA Song, SR Chowdhury, M Malekzadeh, S Harrison… - Plos one, 2023 - journals.plos.org
Sleep is an important indicator of a person’s health, and its accurate and cost-effective
quantification is of great value in healthcare. The gold standard for sleep assessment and the …
quantification is of great value in healthcare. The gold standard for sleep assessment and the …
PET image deblurring and super-resolution with an MR-based joint entropy prior
The intrinsically limited spatial resolution of positron emission tomography (PET) confounds
image quantitation. This paper presents an image deblurring and super-resolution …
image quantitation. This paper presents an image deblurring and super-resolution …
[HTML][HTML] Longitudinal predictive modeling of tau progression along the structural connectome
Tau neurofibrillary tangles, a pathophysiological hallmark of Alzheimer’s disease (AD),
exhibit a stereotypical spatiotemporal trajectory that is strongly correlated with disease …
exhibit a stereotypical spatiotemporal trajectory that is strongly correlated with disease …
Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis
…, T Barbour, J Camprodon, S Chowdhury… - Psychological …, 2024 - cambridge.org
BackgroundElectroconvulsive therapy (ECT) is the most effective intervention for patients
with treatment resistant depression. A clinical decision support tool could guide patient …
with treatment resistant depression. A clinical decision support tool could guide patient …
A longitudinal model for tau aggregation in Alzheimer's disease based on structural connectivity
F Yang, SR Chowdhury, HIL Jacobs… - … Processing in Medical …, 2019 - Springer
Tau tangles are a pathological hallmark of Alzheimer’s disease (AD) with strong correlations
existing between tau aggregation and cognitive decline. Studies in mouse models have …
existing between tau aggregation and cognitive decline. Studies in mouse models have …
Super-resolution PET using a very deep convolutional neural network
The quantitative accuracy of PET is degraded by partial volume effects caused by the
limited spatial resolution capabilities of PET scanners. In this paper, we present a resolution …
limited spatial resolution capabilities of PET scanners. In this paper, we present a resolution …
A physics-informed geometric learning model for pathological tau spread in alzheimer's disease
Tau tangles are a pathophysiological hallmark of Alzheimer’s disease (AD) and exhibit a
stereotypical pattern of spatiotemporal spread which has strong links to disease progression …
stereotypical pattern of spatiotemporal spread which has strong links to disease progression …