Deep learning method segments LA and PPVs from MRI for cardiac disease analysis.
problem Quantitative analysis of left atrium and proximal pulmonary veins from MRI.
method Multi-view CNN with adaptive fusion and loss function for efficient and accurate segmentation.
result Proposed method achieved state-of-the-art sensitivity, specificity, and precision in cardiac segmentation challenge.
Paper proposes DCDG for semi-supervised learning with MRI data.
problem Lack of labeled data and distribution mismatch between labeled and unlabeled data.
method Double-sided domain adaptation for feature space fusion and indirect learning for discriminativeness.
result Proposed DCDG achieves compelling segmentation results for LGE-CMRI images.
V-FCNN neural network segments atria from clinical images.
problem Automatic segmentation of atria from high-resolution images.
method Volumetric Fully Convolution Neural Network (V-FCNN) with a mixed loss function.
result 92.5% Dice similarity coefficient in 54 patients.
Propagating uncertainty improves deep learning model performance.
problem Improving computer-aided detection of pulmonary nodules.
method Multi-stage Bayesian CNN architecture with uncertainty propagation.
result Improves overall performance in terms of accuracy and model confidence.
Pipeline detects pulmonary embolisms from sparse CT images.
problem Manual diagnosis of pulmonary embolisms is laborious and prone to errors.
method Two-stage pipeline using AI, sparse annotations, and robust models.
result Achieved AUC scores of 0.94 on validation and 0.85 on test sets for severe PEs.
This work introduces an integrative approach based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The objective of the application of neural hyper-network to pulmonary embolism (PE) is to improve diagnose for reducing …
Reduces false positives in lung nodule detection by using unlabeled data.
problem Lack of labeled data for training supervised algorithms in medical imaging.
method Uses pseudo-negative labels from unlabeled data to refine a pulmonary nodule detection network.
result False positive rate reduced from 0.4864 to 0.1266 while maintaining sensitivity.
3D G-CNNs reduce false positives in lung nodule detection.
problem Reducing false positives in pulmonary nodule detection.
method Used 3D roto-translation group convolutions (G-Convs) instead of traditional convolutions.
result 3D G-CNNs achieved FROC scores close to those of a CNN trained on ten times more data.
Modeling disease progression in irregularly observed patients.
problem Irregular patient observation in healthcare databases.
method Continuous-time hidden Markov model with generalized linear model.
result Interpretable model of healthcare utilization events.
Study uses multi-task Bayesian optimization to speed up SVM hyperparameter tuning for nodules diagnosis.
problem Redundant and time-consuming hyperparameter tuning for SVM classifiers in medical imaging.
method Employed multi-task Bayesian optimization to accelerate hyperparameter search.
result Multi-task Bayesian optimization significantly accelerates hyperparameter search.
Reinforcement learning improves self training for medical image segmentation.
problem Lack of labeled data in medical imaging.
method Integrating reinforcement learning into self training for complex segmentation networks.
result Improved segmentation performance with less labeled data.
Smartphone app diagnoses pulmonary diseases from chest X-rays.
problem Scarcity of training data and class imbalance issues.
method Data Augmentation Generative Adversarial Network (DAGAN) and Convolutional Siamese Network with attention mechanism.
result Achieved 99.30% and 98.40% testing accuracy on Binary/Multiclass scenarios.
Deep neural network detects lung nodules in CT scans.
problem Challenging and time-consuming manual assessment of CT images for pulmonary nodules.
method ReCTnet combines convolutional and recurrent layers to learn from CT slices.
result ReCTnet achieves 90.5% detection sensitivity with 4.5 false positives per scan.
Researchers create a teapot model for Mandelbrot set, proving connectedness.
problem Understanding the connectedness of the outside part of the Mandelbrot set.
method Defined a 'Master teapot' model and generalized kneading theory for principal veins.
result Outside part of the 'Thurston set' is path connected.
CNNs accurately measure airways and vessels on CT images, improving lung disease diagnosis.
problem Accurately characterizing small pulmonary structures from CT images for disease diagnosis.
method Generative model combined with Convolutional Neural Regressor (CNR) for cross-sectional measurements.
result CNNs provide accurate measurements with physiological correlates.
Improves Bayesian optimization for expensive function evaluations.
problem Optimizing expensive functions with noisy, non-differentiable outputs.
method Proposes a new improvement-based acquisition function.
result Performs at least as well as state-of-the-art acquisition functions, often better.
SAPSAM trains CNNs on lung CTs with binary labels, improving CPA detection and localization.
problem Chronic Pulmonary Aspergillosis (CPA) detection and localization on CT scans using binary labels.
method Binary labels, average intensity projections, 2D RGB-like images, hierarchical CNN architectures.
result High classification accuracy, precise localization, predictive power of 2-year survival.
A new method classifies lung images for COPD using multiple instance learning.
problem Early detection of COPD from lung images with weakly labeled data.
method Multiple Instance Learning (MIL) approach to quantify COPD from weakly labeled patches of CT images.
result The best method based on averaging instances achieves an AUC of 0.742, significantly higher than previous results.
System converts 3D lung nodule images into embeddings for retrieval.
problem Retrieving similar 3D lung nodule images for radiologist decision support.
method 3D deep learning, semantic representation, transfer learning, similarity score.
result System can measure similarity between nodule annotations and CBIR results.
Introduces Star-Shaped deviation measures for risk analysis.
problem Risk measurement and analysis in finance.
method Characterizes Star-Shaped deviation measures through acceptance sets and convex deviation measures.
result Exposes the relationship between Star-Shaped risk measures and deviation measures.
Generative Adversarial Model improves RV segmentation accuracy in MRI.
problem Accurate segmentation of the right ventricular blood pool from cine MRI sequences.
method Combines FCNN, Gated Recurrent Units (GRU), Generative Adversarial Networks (GAN), and L1 loss function.
result Improves Dice Index and Hausdorff Distance by 0.05 and 3.49 mm respectively.
Lie algebroids are by no means natural as an infinitesimal counterpart of groupoids. In this paper we propose a functorial construction called Nishimura algebroids for an infinitesimal counterpart of groupoids. Nishimura algebroids, intended for differential geometry, are of the same vein as Lawvere's functorial notion…
The E_8-manifold has several natural framed link descriptions, and we give an efficient method (via `grapes') for showing that they are indeed the same 4-manifold. This leads to explicit handle pictures for the perturbation of singular fibers in an elliptic surface to a collection of fishtails. In the same vein, we sho…
New inexact proximal gradient methods solve non-convex optimization problems.
problem Solving non-convex optimization problems with non-smooth regularization.
method Proposed three inexact proximal gradient algorithms, including basic and Nesterov's accelerated versions.
result Theoretical analysis shows convergence rates similar to exact methods.
Improves time series classification with forest proximities.
problem Time series classification accuracy and efficiency.
method PF-GAP, an extension of RF-GAP proximities to proximity forests, combined with Multi-Dimensional Scaling and Local Outlier Factors.
result Forest proximities show stronger connection between misclassified points and outliers.
Improved sampling guarantees for weakly log-concave distributions.
problem Sampling from distributions that are not strongly log-concave.
method Proximal sampler with convergence guarantees under weaker assumptions.
result New state-of-the-art sampling guarantees for various target distributions.
In the vein of Bonfert-Taylor, Bridgeman, Canary, and Taylor we introduce the notion of quasiconformal homogeneity for closed oriented hyperbolic surfaces restricted to subgroups of the mapping class group. We find uniform lower bounds for the associated quasiconformal homogeneity constants across all closed hyperbolic…
WEST uses EHRs and expert cases to improve rare disease phenotyping.
problem Limited labeled data for rare diseases.
method Weakly supervised transformer model trained on probabilistic silver-standard labels.
result WEST outperforms existing methods in phenotype classification and subphenotyping.
CFR-Pro enhances treatment effect estimation by incorporating local proximity.
problem Treatment selection bias in HTE estimation from observational data.
method Proximity-enhanced CounterFactual Regression (CFR-Pro) with pair-wise proximity regularizer and subspace projector.
result Significantly outperforms competitors in HTE estimation accuracy.
Improved random forest proximities capture data geometry.
problem Inaccurate random forest proximities do not reflect learned data geometry.
method Introduce RF-GAP: Geometry- and Accuracy-Preserving proximities.
result RF-GAP improves geometric representation in tasks like data imputation.
Framework analyzes leaf vein architecture using deep learning and statistical methods.
problem Discards structural information in leaf venation studies.
method Integrates deep learning and statistical techniques to represent and analyze leaf vascular architecture.
result Identifies significant gene-environment interactions in leaf vascular architecture.
Introduces PPMM algorithm for nonconvex robust regression problems.
problem Nonconvex tuning-free robust regression problems.
method PPMM algorithm with inner subproblems solved by SSN-PPA.
result Converges to d-stationary point with KL property.
Paper analyzes convergence of proximal algorithm in metric spaces without geodesic convexity.
problem Analyzing convergence of proximal algorithm in general metric spaces.
method Analysis of the Wasserstein proximal algorithm without geodesic convexity assumption.
result Establishes unbiased and linear convergence rate for proximal algorithm under natural Wasserstein inequality.
Proximal splitting methods solve rank-constrained convex problems locally.
problem Solving optimization problems with rank constraints.
method Proximal splitting algorithms with conditions on rank constraint convex envelopes.
result Proximal splitting methods converge locally to solutions under convex relaxation conditions.
Extends RF proximities to all supervised distance-based machine learning contexts.
problem Limited utility of RF proximities in various machine learning tasks.
method Introduces generalized Proximity Forest (PF) model and variant for regression.
result Demonstrates unique advantages over RF and k-nearest neighbors models.
Proposes a tool to contrast global vs personalized models in clinical prediction.
problem Balancing global vs personalized models in clinical prediction.
method Localized regression approach using autoencoder for dimension reduction.
result Identification of patient subgroups where global models fall short.
Improved bounds for proximal gradient algorithms with computational errors.
problem Analyzing convergence of proximal gradient algorithms with inaccuracies.
method Deriving new tighter deterministic and probabilistic bounds for convex composite problems.
result Probabilistic bounds are more robust and accurate for algorithm verification and performance guarantees.
Paper extends theorem on covering spaces and Jordan curves.
problem Covering and extending theorems for Alexandrov spaces.
method Introduces proximal homotopic cycles to extend the Mitsuishi-Yamaguchi theorem.
result Extensions of the Mitsuishi-Yamaguchi Good Covering Theorem and Jordan curve theorem.
Inertial proximal gradient algorithm shows monotonically decreasing values.
problem Optimizing functions with inertia.
method Proximal gradient algorithm with alternated inertia.
result Algorithm with alternated inertia achieves monotonically decreasing functional values.
Proximal algorithms applied to current deformation into cycles.
problem Deformation of de Rham currents into cycles.
method Proximal algorithms, total variation denoising for differential forms.
result Calibrated cycles constructed in calibrated manifolds.
Innovative method solves nonconvex optimization on manifolds.
problem Nonconvex optimization problems on Riemannian manifolds.
method Intrinsic Riemannian proximal gradient method.
result Converges for nonconvex or nonembedded problems.
New PnP algorithm converges with relaxed proximal gradient descent.
problem Convergence issues in PnP methods with deep denoisers.
method Relaxed proximal gradient descent for PnP with weakly convex regularization.
result Proposed PnP-αPGD converges for a wider range of regularization parameters. The paper connects a proximal method to stochastic filters and Bayes updates.
problem Large-scale optimization and probabilistic methods for regression.
method Explicit form of Bayes updates for linear regression and general sequential setting.
result The incremental proximal method can be realized by the Kalman filter for linear-quadratic cost functions.
Study uses weak labels and visual attention networks to detect lung nodules in chest radiographs.
problem Automated detection of lung nodules in chest radiographs requires large amounts of manually annotated images.
method Proposes two network architectures: one using saliency maps and the other a recurrent attention model trained with reinforcement learning.
result Demonstrates promising nodule detection performance using weak labels and visual attention mechanisms.
EPINE enhances network embedding by improving adjacency matrix-based high-order proximity.
problem Inaccurate and poorly designed calculation of high-order proximity in network embedding.
method EPINE redefines high-order proximity intuitively and proposes a scalable algorithm for accurate calculation.
result EPINE outperforms existing methods in network reconstruction, link prediction, and node classification.
Stochastic version of proximal distance algorithm analyzed and validated.
problem Optimization of constrained estimation problems.
method Stochastic proximal distance algorithm, with convergence guarantees and finite error bounds.
result Convergence guarantees and finite error bounds for the first time.
Proximal boosting improves gradient boosting for non-differentiable losses.
problem Minimizing non-differentiable losses in prediction models.
method Proximal point algorithm applied to gradient boosting.
result Proximal boosting outperforms gradient boosting in convergence rate and accuracy.
Study uses data to analyze COPD patients' impact on hospital systems.
problem Understanding and quantifying resource requirements for COPD patients.
method Combines segmentation, queuing theory, and data recovery techniques.
result Finding useful operational results from incomplete administrative data.