End-to-end lung nodule characterization using CNN and GP.
problem Fast and robust computer aided system for lung nodule classification.
method Multi-view CNN with data augmentation and Gaussian Process regression.
result Significant improvement over other methods in malignancy determination.
Generates synthetic lung nodule images for training.
problem Lack of source image data for training machine learning models.
method Autoencoder techniques for 3D shape generation.
result Produces high-quality synthetic 3D images.
NoduleX predicts lung nodule malignancy with high accuracy using CT scans.
problem Challenges in accurately predicting lung nodule malignancy from CT scans.
method Deep learning convolutional neural networks (CNN) trained on a large dataset of lung nodules.
result NoduleX achieves an AUC of ~0.99 for nodule malignancy classification, comparable to radiologists.
A novel approach for 3D lung nodule segmentation using adaptive ROI and multi-view residual learning.
problem Inaccurate nodule segmentation due to fixed ROI and redundant structures.
method Two-stage approach: 2D ROI patch-wise investigation with adaptive ROI strategy, followed by 2D and 3D VOI investigation with deep residual U-Net.
result Significantly robust and accurate nodule segmentation compared to previous methods.
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.
GD networks improve lung nodule classification accuracy.
problem Difficult classification of lung nodules of varying sizes.
method Proposes Gated-Dilated (GD) networks with Context-Aware sub-network.
result GD network outperforms state-of-the-art models with AUC > 0.95.
U-Det improves lung nodule segmentation in CT images.
problem Challenging shapes and surroundings of lung nodules in CT images.
method End-to-end deep learning with Bi-FPN, Mish activation, and class weights.
result U-Det achieves 82.82% Dice similarity coefficient, comparable to human experts.
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.
3D Axial-Attention improves lung nodule classification accuracy.
problem Limited 3D attention in existing methods.
method Proposes 3D Axial-Attention network with 3D positional encoding.
result 3D Axial-Attention achieves state-of-the-art performance.
Paper classifies lung nodules in CT scans, improving diagnostic accuracy.
problem Early detection of lung nodules for better treatment planning.
method Proposes four 3D neural networks for direct mapping from 3D images to class labels.
result 3D multi-output DenseNet achieves state-of-the-art classification accuracy.
Deep Local-Global network improves lung nodule malignancy prediction.
problem Challenging task of classifying lung nodules as benign or malignant.
method Proposes a novel method combining local and global feature extraction.
result Achieved state-of-the-art results with AUC=95.62%.
S4ND detects lung nodules faster and more accurately.
problem Efficient lung nodule detection from CT scans.
method Single-Shot Single-Scale 3D Convolutional Neural Network (CNN) trained end-to-end.
result S4ND outperforms state-of-the-art methods in terms of efficiency and accuracy.
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.
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.
End-to-end lung nodule detection system improves sensitivity and performance.
problem Detecting subtle lung nodules in raw CT data.
method Deep reconstruction network followed by 3D-CNN for nodule detection.
result End-to-end detector achieves comparable sensitivity to fully-sampled data.
Paper reduces false positives in lung nodule detection using deep learning on point clouds.
problem Reduces false positives in lung nodule detection.
method Uses deep learning models for point clouds to transform 3D CT scan data.
result Achieved 85.98 FROC compared to 77.26 FROC for baseline models.
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.
Clearing algorithm improves CT image segmentation accuracy by merging confident annotations.
problem Inaccurate predictions due to noisy annotations from different annotators.
method 3-stage algorithm: scoring annotators, scoring nodules, merging annotations.
result Improves prediction accuracy in CT image segmentation tasks.
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.
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.
End-to-end CAD system for thyroid nodule classification using multimodal data and expert guidance.
problem Improving accuracy in thyroid nodule classification for clinicians.
method Knowledge-driven DenseNet framework using multimodal ultrasound data and expert cues.
result The proposed system achieves relevant performances in thyroid nodule classification.
Synthetic sampling improves per-class performance in multi-label malignancy prediction.
problem Imbalanced multi-label classification problem in CADx systems.
method Synthetic oversampling techniques using random forest classifier.
result Average 7.22% point increase in sensitivity for minority classes.
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.
New method uses probabilistic independence to discover disease signatures from medical records.
problem Insufficiently precise diagnosis of clinical disease leading to treatment failures.
method Unsupervised machine learning using probabilistic independence to disentangle disease patterns.
result Inferred 2000 clinical disease signatures from medical records, improving cancer prediction.
UNet++ improves medical image segmentation with deep supervision.
problem Improving accuracy in medical image segmentation.
method Nested U-Net architecture with deep supervision.
result UNet++ achieves significant improvements in IoU scores.
Improves uncertainty estimates in lung node segmentation.
problem Inaccurate uncertainty estimates in medical segmentation.
method Supervised learning using multi-grader annotation variability.
result Improves predictive uncertainty estimates and sample diversity.
We correct for sampling bias in training models to improve real-world performance.
problem Sampling bias causes discrepancies between lab and real-world model performance.
method Bayesian risk minimization and derived bias-corrected loss functions.
result Our approach integrates seamlessly into current learning paradigms and improves model performance.
Method learns feature maps from deep CNN layers for weakly supervised chest pathology localization.
problem Localization of chest pathologies in X-ray images is challenging due to varying sizes and appearances.
method Class-aware deep multiscale feature learning using intermediate feature maps from CNN layers.
result Improves localization performance of small pathologies like nodules and masses.
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.
Proposes ConRad model for lung cancer classification using radiomics and interpretable machine learning.
problem Lack of interpretability in deep neural networks for cancer diagnosis.
method Integration of radiomics and DNN-predicted biomarkers in interpretable classifiers (ConRad).
result ConRad models outperform CNNs in five-fold cross-validation.
Paper characterizes generic transversality, improving on Mather's result.
problem Understanding and defining generic transversality.
method Characterization of transversality based on Mather's work.
result Improves on Mather's transversality result.
We extend Howie's characterization of alternating knots to give a topological characterization of toroidally alternating knots, which were defined by Adams. We provide necessary and sufficient conditions for a knot to be toroidally alternating. We also give a topological characterization of almost-alternating knots whi…
Study characterizes Einstein metrics in warped product spaces.
problem Characterizing Einstein metrics in warped product spaces.
method Local characterizations and global restatements of known results.
result Restated global characterizations of Einstein manifolds.
The study provides homological characterizations for Q-manifolds and l2-manifolds.
problem Density of maps in characterizing Q-manifolds and l2-manifolds. method Investigates weakening the density of Zn-maps and Z-maps to homological maps. result Obtains homological characterizations for Q-manifolds and l2-manifolds. The paper characterizes Alexander quandles of finite groups.
problem Characterizing Alexander quandles of finite groups.
method Using group theory and automorphism groups, the paper provides characterizations of Alexander quandles.
result Generalized Alexander quandles of finite groups are characterized in terms of automorphism groups and underlying groups.
No single parameter characterizes the learnability of probability distributions.
problem Finding a parameter to characterize the learnability of probability distributions.
method Analyzing various notions of learnability and showing impossibility results.
result No such parameter exists for characterizing learnability of probability distributions.
Generalizing Howie and Greene's characterization of alternating knots, we give a topological characterization of almost alternating knots.
Characterizes the OU matrix for up to 5 strands in braids.
problem Understanding the structure of braid diagrams through their matrices.
method Characterization of the OU matrix for up to 5 strands in braids.
result Standard form of the OU matrix for general braids of up to 5 strands is given and characterized.
Study provides concrete examples of knot slopes.
problem Finding explicit characterizing slopes for knots.
method Concrete examples for the (-2,3,7)-pretzel knot.
result Explicit characterizing slopes for the knot 12n242. Characterizes the sample complexity of list regression tasks.
problem Understanding the sample complexity of list learning tasks in regression.
method Introducing two combinatorial dimensions: k-OIG dimension and k-fat-shattering dimension.
result These dimensions characterize realizable and agnostic k-list regression.
Characterizes geodesic laminations on surfaces.
problem Understanding geodesic laminations on surfaces.
method Topological characterization of geodesic laminations.
result Proved a topological characterization of geodesic laminations.
Study characterizes geodesic ray transform on surfaces, isolating and separating sub-ranges.
problem Characterizing the range of the attenuated geodesic ray transform on surfaces.
method Isolating and separating sub-ranges of sums of functions and one-forms, deriving new inversion formulas.
result Range characterizations and new inversion formulas for geodesic ray transform.
We establish a characterization of adequate knots in terms of the degree of their colored Jones polynomial. We show that, assuming the Strong Slope conjecture, our characterization can be reformulated in terms of "Jones slopes" of knots and the essential surfaces that realize the slopes .For alternating knots the refor…
Study characterizes specific almost Kenmotsu metrics meeting Miao-Tam equation.
problem Characterizing almost Kenmotsu metrics.
method Characterization through Miao-Tam equation.
result Characterized specific almost Kenmotsu metrics.
In this paper, we give some characterizations for spacelike helices in Minkowski space-time. We find the differential equations characterizing the spacelike helices and also give the integral characterizations for these curves in Minkowski space-time.
Research characterizes critical points of scalar curvature functionals.
problem Characterizing critical points of scalar curvature functionals.
method Translation and analysis of a previous Russian paper.
result Provides insights into critical points of scalar curvature functionals.
We review several results related to the characterization of polyhedra in hyperbolic 3-space. In particular we present Rivin's theorem that gives a characterization of compact convex hyperbolic polyhedra, and Hodgson's proof of the Adreev's theorem. We also review the analogous characterization of ideal polyhedra, and …
It is of interest to characterize algebraically the dynamical types of isometries of the complex and quaternionic hyperbolic planes. In the complex case, such a characterization is known from the work of Giraud-Goldman. In this paper, we offer an algebraic characterization of the isometries of the two-dimensional quate…