CNNs improve automatic femur bone segmentation from MR images.
problem Manual segmentation of bone MR images is time-consuming and impractical.
method Deep convolutional neural networks (CNNs) trained on volumetric structural MR images of the proximal femur.
result CNNs achieved high segmentation accuracy (dice similarity score of 0.94±0.05). Deep morphing detects bone structures in low-quality X-ray images.
problem Detecting bone structures in low-quality fluoroscopic X-ray images.
method Two-stage deep learning approach using deep segmentation networks and statistical shape models.
result Efficiently detects bone structures in low-quality X-ray images.
Six AI solutions accurately detect growth plate planes in mice bone scans.
problem Manual, time-consuming, and variable bone growth plate detection in micro-CT scans.
method Prepared and annotated a dataset of 3D μCT scans, organized a challenge, and developed six computer vision solutions.
result Achieved mean absolute error of 1.91±0.87 planes from ground truth.
Deep-learning method estimates bone 3D structure from X-ray images.
problem Estimating bone 3D structure from X-ray images.
method Triplet loss-trained neural network selecting closest 3D bone shape from predefined set.
result Average RMS distance of 1.08 mm between predicted and true shapes.
Characterizes graphs with Lin-Lu-Yau curvature at least one and explores bone-idle graphs.
problem Characterizing graphs with specific curvature properties.
method Study of Ollivier-Ricci curvature and Lin-Lu-Yau curvature, exploration of regular graphs, and exact formula derivation.
result Characterizes edges that are bone-idle in regular graphs and provides a complete characterization of 4-regular bone-idle graphs.
Generative model boosts bone lesion classification with augmented data.
problem Class imbalance and limited training data for bone lesion classification.
method Cycle-consistent GAN for image patch translation across bones.
result Generative augmentation improves classifier performance on held-out test set.
Paper uses deep learning to suppress bones on chest X-rays.
problem Improving pathologies classification by suppressing bones on chest X-rays.
method Conditional Generative Adversarial Network (GAN) and Haar 2D wavelet decomposition.
result Achieves state-of-the-art performance on bone suppression.
Efficient deep learning model classifies fractures from X-rays.
problem Manual X-ray examination of fractures is time-consuming and error-prone.
method Robust training loop using transfer learning and latest dataset.
result Model achieves superior performance in less than ten epochs.
Modeling bone microarchitecture adaptation using geometric flows.
problem Bone microarchitecture adaptation modeling.
method Advection and mean curvature flow model with a sphere as a test case.
result Closed-form solution for sphere under advection and mean curvature flow.
RRN accurately identifies landmarks in CMF bones without segmentation.
problem Accurately identifying landmarks in craniomaxillofacial bones without segmentation.
method End-to-end RRN architecture using dense-block units for landmark imputation.
result RRN achieves <2 mm RMS error in landmarking.
Curvature formulas on regular graphs identified bone idle edges and graphs.
problem Understanding curvature in regular graphs and identifying bone idle edges.
method Explicit formulas for Lin-Lu-Yau and Ollivier-Ricci curvatures derived from graph parameters.
result Equality condition on regular graphs for Ollivier-Ricci curvature and characterization of bone idle edges.
This paper uses deep learning to estimate flow fields from OCT images for laser ablation control.
problem Automatic control of laser bone ablation using 4D OCT images.
method Semi-supervised convolutional neural network for 2.5D scene flow estimation.
result Scene flow estimation enables markerless tracking and automated laser ablation control.
A new algorithm discovers causal factors between T2DM and bone mineral density.
problem Discovering causal factors between T2DM and bone mineral density from clinical data.
method Prior-Knowledge-driven local Causal structure Learning (PKCL) algorithm.
result PKCL achieves more reliable results without long-standing medical experiments.
Bayesian CNN estimates uncertainty in bone age prediction.
problem Uncertainty quantification in age estimation models.
method Variational Inference for Bayesian CNNs.
result Model uncertainty distinguished from data uncertainty.
The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilage with diffuse boundaries and inhomogeneous intensities. In this paper, we present an iterative multi-class learning method to segment the femoral, tibial and patellar car…
ChronoMID uses neural networks to classify bone disease in mice from micro-CT scans.
problem Classifying bone disease in mice from micro-CT scans.
method ChronoMID applies cross-modal convolutional neural networks to incorporate temporal information from timestamps and difference images.
result The top-performing model achieved 99.54% accuracy, significantly outperforming a baseline CNN.
Concept bottleneck models enable concept manipulation for model interpretation.
problem Training models to directly predict labels without intermediate concepts.
method Train models to predict intermediate concepts at training time, then use these concepts to predict labels.
result Concept bottleneck models achieve competitive accuracy with standard models while enabling concept manipulation.
Unified framework for Bayesian online learning in changing conditions.
problem Probabilistic online learning in non-stationary environments.
method BONE framework with three modelling choices and two algorithmic choices.
result Framework allows for reinterpreting and proposing new methods.
Model learns to focus on key areas of chest X-rays.
problem Detecting abnormalities in chest X-rays.
method Recurrent visual attention model using reinforcement learning.
result Model can focus on informative areas of X-rays.
Method distinguishes genetic correlations from causation in GWAS.
problem Identifying causal relationships among genetically correlated traits.
method Mixed fourth moments to quantify causal relationships.
result Identified 30 putative genetically causal relationships across 52 traits.
Paper uses deep reinforcement learning for personalized medical treatment recommendations.
problem Personalized treatment recommendations for medical conditions.
method Deep reinforcement learning framework for dynamic treatment regimes.
result Demonstrated promising accuracy in predicting expert decisions and expected rewards.
Optimization framework for reconstructing missing mandible segments.
problem Missing mandible geometry in jaw reconstructive surgeries.
method Conditional variational autoencoder (CVAE) with weighted multi-target probabilistic solution.
result Statistically significant performance improvement over CVAE.
Automatic segmentation of auditory ossicles from CT images using Ricci curvature.
problem Automatic diagnosis of ossicles' diseases from 3D CT images of the head.
method Proposes a completely automatic method that locates and segments ossicles without manual labels or templates, using Ricci curvature in an energy function.
result Performance of the proposed method using discrete Forman-Ricci curvature is superior to state-of-the-art methods.
Transfer entropy analyzes interactions between network communities, including rare events.
problem Understanding information flows between network communities.
method Transfer entropy analysis, including Rényi transfer entropy for rare events.
result Transfer entropy provides a coherent description of community interactions, including non-linear interactions.
CSI-Net learns WiFi signals for body characterization and pose recognition.
problem Unified learning of body characteristics and pose recognition.
method Unified Deep Neural Network (DNN) for WiFi signal representation and multi-task learning.
result CSI-Net solves biometrics estimation and person recognition.
Compare multifractal spectrum estimation techniques in financial data.
problem Estimating multifractal exponents from financial time series.
method Comparison of Rényi entropy-based methods on various financial datasets.
result Rényi entropy methods are effective in heavy-tailed data.
The paper explores three methods to assign a metric to shape spaces.
problem Assigning a Riemannian metric to shape spaces without parameterization.
method Three methods to put a Riemannian metric on shape spaces.
result Methods provide a way to measure deformations independent of parameterization.
Method corrects bias in regression using simulated data and real-world gene expression data.
problem Bias in estimated effect parameters due to misclassification of class labels.
method Simulation and extrapolation method to correct bias.
result Corrected bias in estimated effect parameters.
Computes circular area and spherical volume invariants via integrals.
problem Computing circular area and spherical volume invariants for curves and surfaces.
method Using the Divergence Theorem, express area and volume integrals as line and surface integrals against kernels, then compute analytically on triangulated meshes.
result Simple algorithm for computing spherical volume invariant for triangulated surfaces without discretizing ambient space.
AECF improves multimodal inference robustness and calibration.
problem Robustness and calibration issues in multimodal systems with missing inputs.
method Adaptive Entropy-Gated Contrastive Fusion (AECF) layer.
result Improves masked-input mAP by +18 pp at a 50% drop rate.
New approach to superstatistics shows transitions between regimes in financial data.
problem Restrictions of traditional superstatistics in analyzing complex data.
method Multi-scale generalization of superstatistics.
result Transition between two superstatistics regimes observed in financial data.
Develops a neural surrogate for proton dose calculation using Monte Carlo dropout uncertainty.
problem Computational demand in proton therapy workflows requiring repeated evaluations.
method Integrates Monte Carlo dropout into a neural network surrogate for fast, differentiable dose predictions and uncertainty quantification.
result Shows significant speedups over MC while retaining uncertainty information.
Deep learning model diagnoses celiac disease severity from intestinal biopsy images.
problem Diagnosing celiac disease severity from biopsy images, especially mild cases.
method Deep residual networks trained on a modified Marsh score histological scoring system.
result Model achieved AUC > 0.96 in all classes for CD severity classification.
Genomic predictors explain 40% of human height variance.
problem Missing heritability in human height prediction.
method High-dimensional statistics and machine learning.
result Predicted heights correlate 0.65 with actual height.
AirRL uses RL to infer urban air quality from selected stations.
problem Inferring fine-grained urban air quality from limited monitoring stations.
method Reinforcement learning model with a dynamic station selector and air quality regressor.
result AirRL achieves highest performance in air quality inference experiments.
The paper tackles Bayesian inference with small datasets using manifold learning.
problem Small datasets challenge Bayesian inference with non-Gaussian models.
method Manifold learning and sampling for Bayesian posterior approximation.
result The method effectively samples Bayesian posteriors with non-Gaussian models from small datasets.
Study on sparse recovery with mixed-quality data, establishing sample-size conditions.
problem Sparse recovery with heterogeneous noise from high- and low-quality sources.
method Establishes linear trade-off for sufficient conditions, analyzes LASSO algorithm.
result Linear trade-off for sufficient conditions, robustness of LASSO to data heterogeneity.
Protein structure prediction has been a grand challenge problem in the structure biology over the last few decades. Protein quality assessment plays a very important role in protein structure prediction. In the paper, we propose a new protein quality assessment method which can predict both local and global quality of …
Measures DNA quality degradation effects.
problem Identifying degraded DNA sequence data.
method Novel quality quantification based on intentional degradation effects.
result Quantified measures of degradation can be used for multiple purposes.
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.
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.
Framework improves ML performance by identifying high-quality data.
problem Poor data quality hampers ML performance.
method Intelligent data-centric evaluation framework combining quality measurements and unsupervised learning.
result Framework improves ML system performance in real-world use case.
New method classifies reticulocytes from red blood cells without labels.
problem Classifying reticulocytes from mature red blood cells without fluorescent labels.
method Unsupervised machine learning on morpho-rheological markers.
result Promising results in classifying reticulocytes from mature red blood cells.
This note investigates the causes of the quality anomaly, which is one of the strongest and most scalable anomalies in equity markets. We explore two potential explanations. The "risk view", whereby investing in high quality firms is somehow riskier, so that the higher returns of a quality portfolio are a compensation …
Do-AIQ framework evaluates AI algorithms' quality using DOE.
problem Quality evaluation of AI mislabel detection algorithms.
method Design-of-experiment approach with high-dimensional constraint space design and surrogate modeling.
result Established framework for evaluating AI algorithm quality robustly.
Survey bridges quality control mechanisms and statistical models for crowdsourcing.
problem Crowdsourcing quality control issues in machine learning systems.
method Unified frameworks for quality control mechanisms and statistical models.
result Taxonomies and future directions for quality control research.
Framework evaluates quality of synthetic data generated with differential privacy.
problem Ensuring synthetic data retains statistical quality after applying differential privacy.
method Developed a framework to evaluate synthetic data quality from a practical researcher's viewpoint.
result Synthetic data can be evaluated against training data or underlying populations, and for specific tasks like inference or prediction.
MRI image quality affects statistical and predictive analysis of brain morphology.
problem Impact of MRI image quality on statistical and predictive analysis of brain morphology.
method Systematic testing of image quality on univariate statistics and machine learning classification using three large datasets.
result Low-quality MRI data significantly affects detecting significant sex/gender differences in smaller samples, but not in larger ones.