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.
This study improves lung tumor segmentation in mice MRI scans with nnU-Net, reducing annotation needs.
tBayes-MICE uses Bayesian MICE for time series data imputation.
PS-VAE extracts multi-parameter MRI biomarkers with uncertainty quantification.
Study compares tree-based imputation methods to MICE PMM for missing data.
Deep learning models outperform MICE in large survey imputation but with hyperparameter tuning.
Paper compares MICE-based methods to deep generative models for synthetic data in ratemaking.
Simulation study evaluates tree-based imputation methods for multi-level data.
Given a high-dimensional data set we often wish to find the strongest relationships within it. A common strategy is to evaluate a measure of dependence on every variable pair and retain the highest-scoring pairs for follow-up. This strategy works well if the statistic used is equitable [Reshef et al. 2015a], i.e., if, …
Automated model tracks mouse behavior in home cages.
In exploratory data analysis, we are often interested in identifying promising pairwise associations for further analysis while filtering out weaker, less interesting ones. This can be accomplished by computing a measure of dependence on all variable pairs and examining the highest-scoring pairs, provided the measure o…
Six AI solutions accurately detect growth plate planes in mice bone scans.
In real-world applications, we can encounter situations when a well-trained model has to be used to predict from a damaged dataset. The damage caused by missing or corrupted values can be either on the level of individual instances or on the level of entire features. Both situations have a negative impact on the usabil…
XPDNet wins MRI reconstruction challenge with neural network.
Generative adversarial networks reconstruct MRI images without full data.
New MRI method maps tissue parameters more accurately by ignoring voxel independence.
StaPLR improves Alzheimer's disease classification by identifying important MRI scan types and measures.
Human brain development is rapid during infancy and early childhood. Many disease processes impair this development. Therefore, brain developmental age estimation (BDAE) is essential for all diseases affecting cognitive development. Brain magnetic resonance imaging (MRI) of infants shows brain growth and morphologic pa…
3D CNNs interpret brain MRI differences between men and women.
Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of AD. However, classification performance obtained with different approaches is difficult to compare and these studies are also difficult to reproduce. In the …
CSGM framework applied to clinical MRI data for robust reconstructions.
New neural network improves MRI reconstruction for non-Cartesian data.
In compressed sensing MRI (CS-MRI), k-space measurements are under-sampled to achieve accelerated scan times. CS-MRI presents two fundamental problems: (1) where to sample and (2) how to reconstruct an under-sampled scan. In this paper, we tackle both problems simultaneously for the specific case of 2D Cartesian sampli…
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measure…
New method MRI improves machine learning models' ability to generalize to unseen data.
New method optimizes MRI sampling patterns for faster scans.
Magnetic resonance imaging (MRI) is widely used in clinical practice, but it has been traditionally limited by its slow data acquisition. Recent advances in compressed sensing (CS) techniques for MRI reduce acquisition time while maintaining high image quality. Whereas classical CS assumes the images are sparse in know…
Deep learning improves MRI analysis of MSK disorders.
Volumetric analysis of brain ventricle (BV) structure is a key tool in the study of central nervous system development in embryonic mice. High-frequency ultrasound (HFU) is the only non-invasive, real-time modality available for rapid volumetric imaging of embryos in utero. However, manual segmentation of the BV from H…
Machine learning for ASD diagnosis using morphological MRI networks.
A central question for active learning (AL) is: "what is the optimal selection?" Defining optimality by classifier loss produces a new characterisation of optimal AL behaviour, by treating expected loss reduction as a statistical target for estimation. This target forms the basis of model retraining improvement (MRI), …
Use expert visualization or conventional clinical indices can lack accuracy for borderline classications. Advanced statistical approaches based on eigen-decomposition have been mostly concerned with shape and motion indices. In this paper, we present a new approach to identify CVDs from cine-MRI by estimating large poo…
Image reconstruction from undersampled k-space data has been playing an important role for fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and also shown potential to significantly speed up MR reconstruction with reduced measurements. This article gives an overview of deep learni…
Paper extends 2D ZSAD to 3D MRI without training, achieving robust anomaly detection.
Paper proposes WGAIN for missing feature reconstruction.
Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and research applications. How- ever, acquiring quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at odds with high-resolut…
A method for MRI brain tumor segmentation using feature vectors and kernel dictionary learning.
A deep learning framework segments deep cerebellar nuclei from 7T MRI, improving accuracy and consistency.
Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning. However, these techniques sometimes struggle to reconstruct sharp images that preserve fine detail while maintaining a nat…
This study benchmarks algorithms for automatic segmentation of LGE-MRI images of the left atrium.
Challenge aims to develop automated meningioma MRI segmentation models.
Generative models create synthetic MRI brain scans for research.
Automated detection of MS lesions improves to 67% with 7T MRI.
Enhances deep neural networks for MRI reconstruction by increasing expressivity.
Mild Traumatic Brain Injury (mTBI) is a significant public health problem. The most troubling symptoms after mTBI are cognitive complaints. Studies show measurable differences between patients with mTBI and healthy controls with respect to tissue microstructure using diffusion MRI. However, it remains unclear which dif…
Typical Magnetic Resonance Imaging (MRI) scan may take 20 to 60 minutes. Reducing MRI scan time is beneficial for both patient experience and cost considerations. Accelerated MRI scan may be achieved by acquiring less amount of k-space data (down-sampling in the k-space). However, this leads to lower resolution and ali…
Paper develops a novel kernel-based method for MRI data recovery.
We introduce a model-based reconstruction framework with deep learned (DL) and smoothness regularization on manifolds (STORM) priors to recover free breathing and ungated (FBU) cardiac MRI from highly undersampled measurements. The DL priors enable us to exploit the local correlations, while the STORM prior enables us …
Generative models improve MRI reconstruction by learning image structure.