A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep learning ensemble improves Alzheimer vs. Mild Cognitive Impairment diagnosis.
Most machine learning classifiers give predictions for new examples accurately, yet without indicating how trustworthy predictions are. In the medical domain, this hampers their integration in decision support systems, which could be useful in the clinical practice. We use a supervised learning approach that combines E…
Mild cognitive impairment (MCI) is a prodromal phase in the progression from normal aging to dementia, especially Alzheimers disease. Even though there is mild cognitive decline in MCI patients, they have normal overall cognition and thus is challenging to distinguish from normal aging. Using transcribed data obtained …
Scoping review finds EEG key in MCI research, identifying ERP/EEG, QEEG, and machine learning.
DIM shows promise in predicting Alzheimer's progression.
New method predicts Alzheimer's risk with individual uncertainty estimates.
Model predicts cognitive health risks based on smartphone usage patterns.
A CNN on semi-regular meshes classifies brain diseases from MRI scans.
Multiple modalities of biomarkers have been proved to be very sensitive in assessing the progression of Alzheimer's disease (AD), and using these modalities and machine learning algorithms, several approaches have been proposed to assist in the early diagnosis of AD. Among the recent investigated state-of-the-art appro…
New method handles missing data in multimodal brain imaging.
NEURO-DRAM improves neuroimaging classification accuracy.
THS-GAN uses tensorizing and high-order pooling for AD diagnosis.
For effective treatment of Alzheimer disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. Herein, we developed a novel framework based on a deep convolutional neural network which can predict future cognitive decline in mild cognitive impairment (MCI) patients using…
Nowadays, a lot of scientific efforts are concentrated on the diagnosis of Alzheimer's Disease (AD) applying deep learning methods to neuroimaging data. Even for 2017, there were published more than a hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (…
Wide and deep neural network predicts Alzheimer's progression from shape and clinical data.
We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hi…
Most approaches to machine learning from electronic health data can only predict a single endpoint. Here, we present an alternative that uses unsupervised deep learning to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1908 patients with Mild Cognitive…
Bayesian model detects altered neural circuits in MCI patients.
Deep learning improves AD diagnosis and prognosis from neuroimaging data.
Predict and classify brain image evolution trajectories from a single MRI timepoint.
Study shows cognitive load impacts financial market efficiency, especially for less sophisticated investors.
In this paper, we propose a novel unsupervised learning method to learn the brain dynamics using a deep learning architecture named residual D-net. As it is often the case in medical research, in contrast to typical deep learning tasks, the size of the resting-state functional Magnetic Resonance Image (rs-fMRI) dataset…
Improved CNNs detect Alzheimer's with 14% accuracy boost.
Traditional voxel-level multiple testing procedures in neuroimaging, mostly -value based, often ignore the spatial correlations among neighboring voxels and thus suffer from substantial loss of power. We extend the local-significance-index based procedure originally developed for the hidden Markov chain models, whic…
Early prognosis of Alzheimer's dementia is hard. Mild cognitive impairment (MCI) typically precedes Alzheimer's dementia, yet only a fraction of MCI individuals will progress to dementia, even when screened using biomarkers. We propose here to identify a subset of individuals who share a common brain signature highly p…
Ensemble model predicts AD progression from CN status with high accuracy.
Paper characterizes early-stage dementia signatures from sensor data.
Develops a new feature theory for robust machine learning.
Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a c…
Project predicts Alzheimer's progression using neural networks and novel data processing.
New method predicts AD progression using MEG brain networks.
Study identifies five AD subtypes using graph diffusion and similarity learning.
Study identifies key MRI features for predicting cognitive performance after mTBI.
In this paper, we consider the problem of estimating multiple graphical models simultaneously using the fused lasso penalty, which encourages adjacent graphs to share similar structures. A motivating example is the analysis of brain networks of Alzheimer's disease using neuroimaging data. Specifically, we may wish to e…
Study examines APOE's impact on AD progression using a novel DEBM approach.
The Giroux correspondence and the notion of a near force-free magnetic field are used to topologically characterize near force-free magnetic fields which describe a variety of physical processes, including plasma equilibrium. As a byproduct, the topological characterization of force-free magnetic fields associated with…
The accurate diagnosis and assessment of neurodegenerative disease and traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and functional deficits due to focal axonal swellings (FAS), but it is difficult to deliver a prognosis due to our limited ability to assess damaged neurons at a cellular le…
Unsupervised framework captures acquisition variability in structural connectomes.
Develops variational Bayesian neural network for complex biomedical applications.
Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are characterized by heterogeneity in age, disease span, progression rate, impairment of memory and cognitive abilities. Due to these variabilities, …
Due to the rapid innovation of technology and the desire to find and employ biomarkers for neurodegenerative disease, high-dimensional data classification problems are routinely encountered in neuroimaging studies. To avoid over-fitting and to explore relationships between disease and potential biomarkers, feature lear…
Develops an inverse particle filter for cognitive systems.
3D CNN accurately classifies infant neurodevelopmental age from MRI scans.
We study the effect of impairment on stochastic multi-armed bandits and develop new ways to mitigate it. Impairment effect is the phenomena where an agent only accrues reward for an action if they have played it at least a few times in the recent past. It is practically motivated by repetition and recency effects in do…
Paper learns latent and hierarchical structures in CDMs from data.
Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.
QC-SPHARM detects Alzheimer's Disease early using hippocampal surface geometry.
DNNs improve localization from channel estimates, overcoming practical impairments.