For a smooth manifold we define the Teichmüller space $\cT(M)$ of all Riemannian metrics on and the Teichmüller space $\cT^ε(M)$ of -pinched negatively curved metrics on , where . We prove that if is hyperbolic the natural inclusion $\cT^ε(M)\hookrightarrow\cT(M)$ is, in general, not…
On-device research index
arXiv research
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.
169,341 papers · 148 categories
Trend · papers per month
48 results for “CT generation”
Improved s-CT generation from MRI using Markov random field and NIG distributions.
problem Generating accurate substitute CT images from MRI for attenuation correction and dose planning.
method Introduced flexible mixture models with spatial dependency and NIG distributions. Used a stochastic EM gradient algorithm for efficient parameter estimation.
result Enhanced predictive quality of s-CT images, reducing mean absolute error by 17.9%.
LcGAN generates synthetic CT images for hemorrhagic lesion segmentation.
problem Scarce training data for hemorrhagic lesion segmentation.
method Lesion conditional Generative Adversarial Network (LcGAN) for synthetic image generation.
result Segmentation improved by 12.8% with synthetic data augmentation.
Generative Adversarial Networks generate realistic CT images for data augmentation.
problem Data insufficiency in medical datasets limits DL techniques.
method Unsupervised data-driven approach using GANs to generate 2D CT images.
result GANs can capture global and local CT variabilities.
CT compares two distributions using Bayes' theorem and chain rule.
problem Measuring the difference between two probability distributions.
method Conditional transport (CT) using chain rule and Bayes' theorem.
result CT strikes a good balance between mode-covering and mode-seeking behaviors.
Automates quality control for synthetic CTs generated from MR images.
problem Prevent downstream errors in RT treatment planning from synthetic CTs.
method Ensemble of sCT generators and uncertainty measure based on their disagreement.
result Uncertainty measure can detect input images outside expected MR distribution and sCT images with potential errors.
BCD-Net improves low-dose CT image reconstruction.
problem Challenges in obtaining accurate low-dose CT images.
method Modified iterative regression CNN, BCD-Net, with faster numerical solvers.
result BCD-Net achieves better image quality and generalization than state-of-the-art methods.
New method reduces radiation dose in CT scans while improving image quality.
problem Reducing radiation dose in CT scans while maintaining image quality.
method Combines PWLS and ℓ1 prior with learned sparsifying transform and ADMM algorithm.
result Improves image quality compared to existing methods for sparse-view CT.
Deep Boosted Regression for MR to CT Synthesisphysics.med-ph
Deep neural network improves CT synthesis from MRI.
problem Generating accurate CT images from MRI for PET reconstruction.
method Deep fully convolutional neural network that recursively reduces residuals.
result Decreased PET reconstruction error from 14.3% to 7.2%.
A new cycleGAN architecture reduces memory and parameter requirements for low-dose CT denoising.
problem Efficient unsupervised low-dose CT denoising with minimal memory and parameter usage.
method Single switchable generator using AdaIN layers for efficient training and inference.
result The proposed method outperforms previous cycleGAN approaches with half the parameters.
Let $\cT$ be Teichmüller space of a closed surface of genus at least 2. For any point $c\in \cT$, we describe an action of the circle on $\cT\times \cT$, which limits to the earthquake flow when one of the parameters goes to a measured lamination in the Thurston boundary of $\cT$. This circle action shares some of the …
Using a Generative Adversarial Network for CT Normalization and its Impact on Radiomic Featureseess.IV
GAN normalizes CT scans for consistent radiomic feature values.
problem Variations in dose levels and slice thickness affect radiomic features sensitivity.
method Used a 3D generative adversarial network (GAN) to normalize reduced dose, thick slice images to normal dose, thinner slice images.
result GAN-based approach led to significantly smaller error in radiomic features.
A new COVID-19 CT dataset helps develop AI diagnosis models.
problem Lack of publicly available COVID-19 CT datasets due to privacy issues.
method Built an open-sourced COVID-CT dataset and developed AI diagnosis methods.
result Developed AI diagnosis models achieving high accuracy and performance.
NACT improves tensor regression predictions with regularization.
problem Improving tensor regression predictions with structural information.
method Noise-Augmented regularization on Tucker decomposition.
result Achieves exact regularization on core tensor in linear and generalized linear tensor regression.
A cascaded CNN reduces artifacts in low-dose CT image denoising.
problem Reduce artifacts in denoised low-dose CT images.
method Cascaded training of CNNs on a dataset to iteratively improve denoising performance.
result The cascaded CNN approach significantly reduces artifacts in denoised CT images.
NIST's CTS Challenge evaluates speaker recognition using telephony data.
problem Improving speaker recognition accuracy using telephony data.
method Large-scale neural network architectures, extensive data augmentation, proprietary data, and fine-tuning.
result Remarkable improvements in speaker recognition performance.
New CT image reconstruction method reduces X-ray dose while improving image quality.
problem Reducing X-ray dose in CT while maintaining image quality.
method Combines PWLS with learned sparsifying transform using alternating optimization and relaxed OS-LALM.
result Proposed method improves image quality for low dose levels compared to existing methods.
Proposes using MR images to create synthetic CT images for prostate segmentation.
problem Creating high-quality annotations for prostate segmentation in CT scans.
method CycleGAN algorithm to create synthetic CT images from MR images, using a 2.5D Residual U-Net for segmentation.
result Automatic delineation of prostate from real CT scans achieved with comparable results to radiologist annotations.
RADNET achieves radiologist-level accuracy in CT scan hemorrhage detection.
problem Automated detection of brain hemorrhages in CT scans.
method RADNET uses a 3D context-aware deep learning model with attention mechanisms.
result RADNET achieves 81.82% accuracy in hemorrhage prediction, comparable to radiologists.
A faster X-ray CT image reconstruction method using relaxed linearized algorithms.
problem Reduced X-ray dose while maintaining image quality in CT scans.
method Relaxed linearized augmented Lagrangian (AL) method with over-relaxation.
result The proposed method is about twice as fast as existing unrelaxed fast algorithms.
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.
New CycleGAN uses invertible generator for faster, less resource-intensive CT denoising.
problem Efficient unsupervised CT denoising without paired data.
method Single generator with wavelet residual domain, no discriminators, cycle consistency via invertible generator.
result Significantly improved denoising performance with faster training and less parameters.
Efficient method predicts coronary calcium scores in cardiac and chest CTs.
problem Quantifying coronary artery calcium for risk assessment.
method Two ConvNets for direct regression of calcium scores, with optional decision feedback.
result Predicted calcium scores are highly correlated with manual scores and provide insight into decision-making.
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.
A new method reduces CT scan radiation exposure while improving image quality.
problem Reducing patient radiation exposure in CT scans while maintaining image quality.
method PWLS-ULTRA method that combines clustering and learning-based techniques.
result The method significantly improves image quality compared to existing methods.
NIST CTS Superset offers a large dataset for telephony speaker recognition.
problem Lack of a large-scale, uniform dataset for telephony speaker recognition.
method Compilation of speech segments from multiple corpora, including Greybeard, Switchboard, and Mixer series.
result Results on the NIST 2020 CTS Speaker Recognition Challenge serve as a reference baseline.
Paper presents a deep learning method for CT image super-resolution.
problem Developing high-quality CT images with reduced radiation exposure.
method Generative adversarial network (GAN) with cycle consistency and residual learning constraints.
result The proposed method achieves accurate, efficient, and robust super-resolution of CT images.
Improved sparse-view CT images with deep learning sinogram interpolation.
problem Sparse-view CT images quality improvement with limited projection data.
method Combination of U-Net and residual learning for sinogram interpolation.
result Significantly improved CT image quality (RMSE and SSIM metrics) over standard methods.
Proposes a method to improve low-dose coronary CT angiography images.
problem Low-dose CT images are degraded due to reduced radiation dose.
method Cycle-consistent adversarial denoising network for semi-supervised learning.
result Significant reduction in noise with preserved texture and edges.
Improved low-dose CT images with deep learning and framelet denoising.
problem Low-dose X-ray CT images often lack texture and detail.
method Proposed a wavelet residual network combining deep learning and framelet denoising.
result Significantly improved performance in preserving image detail.
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.
Paper uses CT-IV to estimate causal effects in non-randomized settings.
problem Estimating causal effects in non-randomized observational studies.
method Modified Causal Tree (CT-IV) algorithm combining CART and IV framework.
result Demonstrates efficiency in handling heterogeneity of causal effects.
Augmented CT slices improve liver lesion classification accuracy.
problem Improving pixel-wise classification of hepatic lesions and normal liver tissues.
method Anatomical data augmentation using adjacent CT slices for training a U-net network.
result Improvement of 3% in success rate, 5% in classification accuracy, and 4% in Dice.
New deep learning methods improve CT image quality from few projections.
problem Sparse-view CT images suffer from streaking artifacts due to limited projections.
method Inspired by deep convolutional framelets, propose new U-Net variants that satisfy the frame condition.
result New U-Net variants provide better reconstruction performance for sparse-view CT.
Study improves CTS's approximation regret for combinatorial bandits.
problem Improving CTS's performance on non-exact oracles.
method Develops a new upper bound for CTS under specific conditions.
result First approximation regret upper bound for CTS.
Deep learning predicts SAH patient mortality from initial CT scans.
problem High mortality rates in SAH patients.
method CNN-based algorithm using transfer learning on CT scans.
result Model accurately predicts mortality (74% accuracy, 82% AUC).
Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstructioneess.IV
DEER network improves few-view breast CT image reconstruction efficiency and quality.
problem Efficient and high-quality few-view breast CT image reconstruction.
method Deep Efficient End-to-end Reconstruction (DEER) network with low model complexity.
result DEER network achieves competitive image quality with significantly fewer parameters compared to state-of-the-art methods.
Public dataset for benchmarking deep learning CT reconstruction methods.
problem Lack of a fair benchmark for comparing deep learning CT reconstruction methods.
method Processed and simulated over 40,000 CT scan slices from the LIDC/IDRI Database.
result First baseline results provided for comparison.
CTS improves combinatorial optimization in unknown environments.
problem Optimizing actions from a large set of alternatives in unknown environments.
method Combinatorial Thompson Sampling (CTS) for semi-bandit feedback.
result CTS achieves optimal regret bounds in various networking problems.
CTS machines improve screen development in printing industries, reducing costs and increasing profitability.
problem Inefficient screen development process in heat transfer printing and labeling.
method Implementation of CTS (computer-to-screen) technology for screen development.
result Reduction in material and operational costs, leading to profitability.
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.
Researchers prove skein relations for tangle Floer homology.
problem Understanding tangle Floer homology through skein relations.
method Combinatorial proof of skein relations for tangle Floer homology.
result Tangle Floer homology satisfies skein relations.
Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal/COVID-19 Classeess.IV
System classifies lung CT scans into normal or COVID-19 using machine learning.
problem Detecting COVID-19 infection in lung CT scans.
method MLS with CBA+KE thresholding, feature extraction, selection, and classification.
result SVM with FFV achieved 89.80% detection accuracy.
A new unsupervised method removes CT metal artifacts using beta-CycleGAN and attention.
problem Metal artifact reduction in computed tomography (CT) images.
method Unsupervised learning using a beta-CycleGAN architecture with attention mechanism.
result Improved metal artifact removal that preserves image details.
Efficient deep learning for CT images reduces memory and training time.
problem Training deep neural networks for CT images is computationally expensive.
method Unrolled proximal gradient descent, replaced penalty terms with CNNs, used greedy learning with deep UNet and surrogate.
result Achieved comparable image quality to state-of-the-art methods on CT image reconstruction challenges.
Z-Net improves 3D CT volume segmentation for surgical planning.
problem Discontinuities and class-imbalances in 3D CT volume segmentation.
method Z-Net uses anisotropic spatial separable convolutions to preserve full field-of-view.
result Z-Net achieves up to 12.6% improvement in IoU for CT segmentation.
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.
The paper studies conditions for Cannon-Thurston maps in trees of hyperbolic spaces.
problem Conditions for existence of Cannon-Thurston maps in trees of hyperbolic spaces.
method Analysis of trees of hyperbolic metric spaces and their subspaces.
result Additional sufficient conditions for the existence of Cannon-Thurston maps.