Sparse random projections simplify complex choice models.
problem Estimating models with large choice sets.
method Sparse random projections followed by cyclic monotonicity moment inequalities.
result The method works well in simulations and real data applications.
EdgeLite detects hazardous supermarket floors, improving safety.
problem Detecting hazardous conditions on supermarket floors to prevent injuries.
method Developed a lightweight deep learning model, EdgeLite, for edge devices.
result EdgeLite outperformed state-of-the-art models in detecting hazards on supermarket floors.
Improved inter-scanner MS lesion segmentation through adversarial training.
problem Variability in MRI scanner or protocol differences affect automated lesion segmentation accuracy.
method Trained a CNN base model and a discriminator model adversarially on multi-scanner longitudinal data.
result Adversarial training improves inter-scanner consistency of lesion segmentations.
New CNN method improves brain MR segmentation across scanners and protocols.
problem Degradation of CNN accuracy on images from different scanners and protocols.
method Lifelong multi-domain learning with shared filters and domain-specific batch normalization.
result Significantly closes the gap to benchmark performance.
Improved image segmentation across scanners using asymmetric weights.
problem Deteriorated performance of classifiers trained on one scanner for segmentation of images from other scanners.
method A weighted ensemble of classifiers trained on individual images, with weights determined by the similarity between training and test images.
result The bag similarity measure is the most robust and achieves excellent results on various brain and white matter lesion segmentation datasets.
We introduce a fully probabilistic framework of consumer product choice based on quality assessment. It allows us to capture many aspects of marketing such as partial information asymmetry, quality differentiation, and product placement in a supermarket.
MRAI-NET learns MRI scanner-independent features for better tissue segmentation.
problem Invariance of MRI voxelwise classifiers across different scanners.
method Siamese neural network to extract acquisition-invariant feature vectors.
result MRAI-NET outperforms traditional classifiers in small sample settings.
Sales data in a commodity market (supermarket sales to consumers) has been analysed by studying the fluctuation spectrum and noise correlations. Three related products (ketchup, mayonnaise and curry sauce) have been analysed. Most noise in sales is caused by promotions, but here we focus on the fluctuations in baseline…
Empirical data of supermarket sales show stylised facts that are similar to stock markets, with a broad (truncated) Levy distribution of weekly sales differences in the baseline sales [R.D. Groot, Physica A 353 (2005) 501]. To investigate the cause of this, the influence of social interactions and advertisements are st…
New method corrects MRI biases across scanners and sites.
problem Site and scanner biases in diffusion MRI data.
method Learning invariant representations using variational auto-encoders (VAE).
result Improvements on test data relative to a baseline method.
Deep learning improves 3D reconstruction from sparse X-ray views.
problem Sparse view CT reconstruction produces severe streaking artifacts.
method Proposes a deep learning architecture for 3D reconstruction from 9 views.
result Superior reconstruction performance confirmed with real data.
Adapts CNN for robust medical image segmentation across different scanners and protocols.
problem Performance degradation of CNNs in medical image segmentation due to mismatch between training and test images.
method Designs a segmentation CNN as a concatenation of a shallow normalization CNN and a deep CNN. At test time, adapts the normalization sub-network for each test image using a denoising autoencoder.
result Consistently improves performance on multi-center MRI datasets of brain, heart, and prostate.
Study assesses CNN model robustness to noise in low-cost CT scans.
problem Evaluate CNN model performance on noisy, artifact-prone low-cost CT images.
method Developed and tested a CNN model for head CT triage, varying tube current and projections.
result Model remains robust to reduced tube current and fewer projections, maintaining AUROC close to original.
MimickNet approximates clinical ultrasound post-processing without proprietary data.
problem Matching proprietary clinical-grade ultrasound post-processing techniques.
method Deep learning framework MimickNet that transforms raw DAS beams into post-processed images.
result MimickNet achieves high SSIM scores (0.930-0.967) on test sets.
Intensity augmentation improves breast MRI segmentation accuracy.
problem Improving segmentation accuracy across different MRI scanners and protocols.
method Applied intensity augmentation in addition to geometric augmentation during training.
result Increased segmentation performance from 0.71 to 0.90.
Proposes evaluating models through posterior dispersion indices.
problem The need for a better model evaluation metric beyond predictive accuracy.
method Introduces posterior dispersion indices (PDI) to evaluate probabilistic models.
result Identifies rich patterns of model mismatch in various real data examples.
Dynamic memory prevents forgetting in continuous learning of medical images.
problem Catastrophic forgetting in machine learning models over time due to domain shifts.
method Dynamic memory to store and replay diverse training data subsets.
result Dynamic memory mitigates forgetting without knowing when shifts occur.
A neural network learns MRI scan-invariant features for brain tissue classification.
problem Lack of generalization in voxelwise classification methods due to scanner differences.
method Siamese neural network (MRAI-net) to learn acquisition-invariant representations.
result Linear classifier outperforms CNNs on limited training data for tissue classification.
Study investigates deep learning model's reliability in clinical MRI data.
problem Tackles reliability of DL models in clinical out-of-distribution MRI data.
method Investigated performance of DL model trained on diverse datasets compared to clinical data.
result Model performs better in similar protocols but worse in clinical data with different tissue contrasts.
Novel method uses image descriptors to harmonize MRI brain volumes across centers.
problem Inconsistencies in MRI brain volume measurements across different centers and scanners.
method Trained a Relevance Vector Machine (RVM) model using image descriptors to harmonize brain volumes.
result Decreases scanner and center variability while preserving measurements for longitudinal studies.
Paper introduces MSP Network for harmonizing images from different scanners.
problem Improving predictive performance and learning efficiency in data harmonization.
method Multi-Stage Prediction (MSP) Network integrating neural networks of different architectures.
result MSP Network shows 20% improvement in patch-based mean-squared error over state-of-the-art methods.
DALES offers a large annotated aerial LiDAR dataset for 3D deep learning.
problem Lack of large-scale annotated aerial LiDAR datasets for deep learning.
method Collection and annotation of over half a billion hand-labeled points from an ALS scanner.
result DALES is the most extensive publicly available ALS data set with improved resolution and coverage.
Study geodesic X-ray transform and streaking artifacts on simple surfaces or spaces of constant curvature.
problem Streaking artifacts in CT images due to metal regions.
method Geodesic X-ray transform on nontrapping compact Riemannian manifolds with strictly convex boundaries.
result Streaking artifacts result from conormal singularities along common tangent geodesics.
KD improves DNN performance on unseen data without hyperparameter tuning.
problem Overfitting in DNNs on unseen data.
method Knowledge distillation for semi-supervised domain adaptation.
result KD achieves significantly higher WMH dice scores than baseline and ADA.
Deep learning predicts brain age from raw MRI data with high accuracy and reliability.
problem Predicting brain age from neuroimaging data to assess individual differences in brain aging.
method Convolutional Neural Networks (CNN) applied to raw T1-weighted MRI data.
result Brain-predicted age is a reliable and heritable biomarker of brain aging.
Unsupervised framework captures acquisition variability in structural connectomes.
problem Acquisition differences across sites, scanners, and protocols complicate structural connectome analysis.
method An unsupervised framework using architectural annealing to balance discrete and continuous latent variables.
result Architectural annealing produces stronger site learning than baseline models.
Study benchmarks methods for learning non-Cartesian k-space trajectories and reconstruction.
problem Benchmarking methods for learning non-Cartesian k-space trajectories and reconstruction.
method Comparing PILOT, BJORK, and HybLearn schemes to learn non-Cartesian k-space trajectories and reconstruction.
result HybLearn scheme outperforms other methods in learning and comparing non-Cartesian k-space trajectories and reconstruction.
Harmonization schemes limit accuracy due to domain information.
problem Harmonization schemes lead to inaccurate predictions due to domain information.
method Analysis of mutual information and real label value informativeness.
result Accuracy is limited by the domain with least information.
This study assesses the reproducibility of 1H-MRS scans across different vendors and sessions.
problem Lack of harmonization in magnetic resonance spectroscopy protocols among vendors.
method Analysis of CV and ICC for within- and between-sessions, and correlation coefficients for across machines.
result Metabolite concentrations are highly reproducible across different vendors and sessions.
New estimators improve sparse semiparametric additive modeling.
problem Sparse semiparametric additive modeling with structured sparsity.
method Combines group subset selection with shrinkage for nonconvex optimization.
result New estimators outperform alternatives in synthetic and real-world data.
Automated MRI image quality assessment framework using machine learning.
problem Manual quality assessment of MRI images is time-consuming and costly.
method Machine learning model trained on human observer labels without reference images.
result Framework achieves 93.7% accuracy in estimating image quality.
Self-supervised method enhances ultrasound images without needing clean targets.
problem Multiplicative speckle, acquisition blur, and scanner artifacts hamper ultrasound interpretation.
method Physics-guided degradation model trained on rotated/cropped patches with synthesized inputs.
result Achieves highest PSNR/SSIM across Gaussian and speckle noise levels, with significant improvements in heavy noise conditions.
Paper uses tensor regression to analyze point clouds for process optimization.
problem Challenges in modeling and analyzing high-dimensional point cloud data.
method Utilizes multilinear algebra and tensor regression techniques.
result Successfully models and links point cloud variational patterns to process variables.
Our analysis of financial data, in terms of super-exponential growth, suggests that the seed of the 2002/03 crisis of the Dutch supermarket giant AHOLD was planted in 1996. It became quite visible in 1999 when the post-bubble destabilization regime was well-developed and acted as the precursor of an inevitable collapse…
New algorithms for CLR with multiple observations and clusters.
problem Finding clusters of entities with minimized overall sum of squared errors.
method Exact mathematical programming, column generation, heuristic algorithms, genetic algorithms, modified Sp{ä}th algorithm.
result Performance comparison of algorithms on SKU clustering problem.
IDA adapts to non-iid data in federated learning for medical imaging.
problem Statistical heterogeneity in federated learning data, especially in medical imaging.
method IDA (Inverse Distance Aggregation) is a novel adaptive weighting approach for clients based on meta-information.
result IDA outperforms Federated Averaging in handling unbalanced and non-iid data in federated learning.
A switchable deep beamformer enables versatile image processing.
problem Training and storing separate beamformers for each application.
method Switchable deep beamformer using Adaptive Instance Normalization (AdaIN) layers.
result Single network can produce various image processing outputs.
This work uses GANs to improve CT image reconstruction from limited angles.
problem Under-determined linear inverse problem in limited angle CT reconstruction.
method Robust GAN prior for image manifold projection.
result Significant improvement in reconstruction quality.
Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or super-resolution, can be addressed by maximizing the posterior distribution of a sparse linear model (SLM). We show how higher-order Bayesian decision-making problems, such as optimizing imag…
We speed up factor analysis on large neuroimaging datasets.
problem Processing large multi-subject neuroimaging datasets efficiently.
method Optimized multi-subject factor analysis methods for parallel processing.
result Strong scaling up to 5.5x with 1024 nodes and 32,768 cores.
The study classifies Android malware using minhashing and Structural Equation Models.
problem Lack of consensus and consistency in malware signatures across different antivirus engines.
method Analyzed over 250k malware signatures from 61 engines on 82k Android apps.
result Identified 41 malware classes grouped into Adware, Harmful Threats, and Unknown categories.
Robots infer distances to invisible obstacles from 2D laser scans.
problem Mobile robots struggle with accurate distance estimation from 2D laser scanners.
method Trained a neural network to map raw 2D laser distances to actual obstacle distances.
result Trained network successfully infers distances from partial 2D laser readings in real-time.
Paper develops PGMM framework for debiased inference on nonparametric IV estimators.
problem Automatic debiased inference on nonparametric IV functionals.
method Penalized GMM (PGMM) framework for functionals of IV estimators.
result PGMM-based debiased estimator performs well, achieving near-nominal coverage.
Neural network predicts intelligence from brain structure measurements.
problem Predicting intelligence scores from brain structure measurements.
method Four-layer fully-connected neural network (FNN) using volumes, WM/GM contrast, and cortical thickness.
result Achieved MSE of 94.0270 in test set.
KD-Net transfers knowledge from multi-modal to mono-modal segmentation networks.
problem Limited acquisition of multiple imaging modalities in clinical settings.
method Generalized distillation framework adapted for mono-modal networks.
result The student network outperforms baseline mono-modal networks in brain tumor segmentation.
PAD offers a principled approach to malware detection against evasion attacks.
problem Machine Learning techniques for malware detection are vulnerable to evasion attacks.
method PAD proposes a new adversarial training framework with convergence guarantees for robust optimization.
result PAD significantly outperforms state-of-the-art defenses and can harden ML-based malware detection against 27 evasion attacks.
New handwritten digits dataset for Kannada script.
problem Lack of datasets for Kannada numeral digits.
method Developed Kannada-MNIST and Dig-MNIST datasets.
result Initial CNN accuracy is lower than MNIST, indicating a challenge in generalization.
A new house price prediction model using location data and multi-task learning.
problem Accurate house price prediction for various stakeholders.
method Location-centered data profiling and Multi-Task Learning (MTL) approach.
result MTL-based methods significantly outperform state-of-the-art approaches in house price prediction.