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.
Study generates synthetic fNIRS data and applies machine learning for improved neuroimaging.
fastHDMI improves neuroimaging variable selection in high-dimensional data.
Deep learning methods quantify uncertainty in neuroimage enhancement.
A new geometric framework embeds correlation matrices into Euclidean space for scalable brain network analysis.
With the arrival of the big data era, more and more data are becoming readily available in various real-world applications and those data are usually highly heterogeneous. Taking computational medicine as an example, we have both Electronic Health Records (EHR) and medical images for each patient. For complicated disea…
Study reveals XAI methods fail in neuroimaging, suggesting domain-specific adaptation.
Deep learning aids in autism diagnosis and rehabilitation using neuroimaging data.
New measures and tests for high-order interactions in complex data.
Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. Despite the many advantages of joint modeling, the standard forms suffer from limitations that arise from a fixed model specification and computational difficulties when applied to …
A test for neural networks identifies genetic associations.
NEURO-DRAM improves neuroimaging classification accuracy.
InVA models image outcomes from multiple modalities, outperforming standard VAEs.
Generative models create synthetic MRI brain scans for research.
BPt is a Python library for ML with neuroimaging data.
Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate…
HBR improves normative modeling of neuroimaging data across multiple sites.
Neuroimaging research has predominantly drawn conclusions based on classical statistics, including null-hypothesis testing, t-tests, and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity, including cross-validation, pattern classification, and sparsity-inducing regression. These t…
Deep learning has shown outstanding performance in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision. The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as r…
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 (…
DeepFDR uses deep learning for better FDR control in neuroimaging data.
Enhanced survival trees improve computational efficiency and inference.
New method quantifies biases in neuroimaging datasets.
The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data sharing re…
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…
Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models. This distinction is based on the insight that brain state features, that are found to be relevant in an experimental paradigm, carry a different meaning in encoding- than in decoding models. In this paper, we a…
Motivated by applications in neuroimaging analysis, we propose a new regression model, Sparse TensOr REsponse regression (STORE), with a tensor response and a vector predictor. STORE embeds two key sparse structures: element-wise sparsity and low-rankness. It can handle both a non-symmetric and a symmetric tensor respo…
Proposes a deep learning framework for interval-censored survival data.
fcHMRF-LIS controls FDR in neuroimaging data, improving power and scalability.
Unified normative modeling for neuroimaging phenotypes using denoising diffusion models.
Novel Bayesian framework for spatio-temporal neuroimaging data.
BrainCast predicts whole-brain fMRI time series from short scans.
Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexi…
This volume is a collection of contributions from the 5th Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI) at the Neural Information Processing Systems (NIPS 2015) conference. Modern multivariate statistical methods developed in the rapidly growing field of machine learning are being increasingly…
A new model for dynamic covariance recovery in neuroimaging data.
This paper describes a new neuroimaging analysis toolbox that allows for the modeling of nonlinear effects at the voxel level, overcoming limitations of methods based on linear models like the GLM. We illustrate its features using a relevant example in which distinct nonlinear trajectories of Alzheimer's disease relate…
Given the wide success of convolutional neural networks (CNNs) applied to natural images, researchers have begun to apply them to neuroimaging data. To date, however, exploration of novel CNN architectures tailored to neuroimaging data has been limited. Several recent works fail to leverage the 3D structure of the brai…
Canonical correlation analysis (CCA) is a valuable method for interpreting cross-covariance across related datasets of different dimensionality. There are many potential applications of CCA to neuroimaging data analysis. For instance, CCA can be used for finding functional similarities across fMRI datasets collected fr…
Novel framework predicts brain biomarker trajectories with superior performance.
A new framework detects statistical significance of deep learning in neuroimaging studies.
In this paper, we propose a capsule-based neural network model to solve the semantic segmentation problem. By taking advantage of the extractable part-whole dependencies available in capsule layers, we derive the probabilities of the class labels for individual capsules through a recursive, layer-by-layer procedure. We…
A new framework for brain mapping using statistical agnostic methods.
Novel algorithm for private analysis of fMRI data.
Develops a semi-supervised learning method to generate missing neuroimaging modalities.
New method handles missing data in multimodal brain imaging.
Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years. In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the di…
Method predicts brain regions for neuroimaging phenotypes.
Selective inference improves multi-task neuroimaging analysis.
Bayesian method for estimating functional graphical models from neuroimaging data.