Automated brain CT image retrieval from traumatic brain injury cohorts using deep neural networks.
problem Manual image retrieval of whole brain CT scans from large clinical cohorts is time-consuming and resource-intensive.
method Proposes a deep convolutional neural network (dMIR) for automated classification of 2D montage images.
result Achieved high accuracy (f1=1.0) for validation and testing data sets.
Deep learning method for brain CT scan anomaly labeling using nearest neighbors.
problem Automated anatomical labeling of brain CT scan anomalies.
method Combines local and global context, uses Relation Networks (RNs) for prediction, and employs nearest neighbors for training.
result Improved performance of Relation Networks (RNs) through nearest neighbors training strategy.
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.
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.
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 CNNs segment heart substructures from MRI and CT scans.
problem Accurate segmentation of heart substructures from radiology scans.
method Multi-planar deep convolutional neural networks (CNNs) with adaptive fusion strategy.
result Precision and Dice Index of 0.93 and 0.90 for CT, and 0.87 and 0.85 for MRI.
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.
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.
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.
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.
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.
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.
Statistical image reconstruction (SIR) methods are studied extensively for X-ray computed tomography (CT) due to the potential of acquiring CT scans with reduced X-ray dose while maintaining image quality. However, the longer reconstruction time of SIR methods hinders their use in X-ray CT in practice. To accelerate st…
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.
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.
3D U-Net improves kidney and tumor segmentation from CT scans.
problem Manual segmentation by clinicians is laborious and error-prone.
method Multi-scale supervised 3D U-Net with deep supervision and post-processing.
result MSS U-Net achieves high Dice coefficients (0.969 for kidney, 0.805 for tumor) on KiTS19 dataset.
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.
Computed tomography (CT) generates a stack of cross-sectional images covering a region of the body. The visual assessment of these images for the identification of potential abnormalities is a challenging and time consuming task due to the large amount of information that needs to be processed. In this article we propo…
A new model improves CT image quality from low-dose scans.
problem Improving CT image quality from low-dose scans.
method Multi-layer Residual Sparsifying Transform (MRST) learning model for low-dose CT reconstruction.
result The MRST model outperforms conventional methods in maintaining subtle details.
Proposes a new model to analyze CT scans for lung cancer patients.
problem Analyzing survival risks of lung cancer patients using CT scans.
method Penalized Deep Partially Linear Cox Model (Penalized DPLC) incorporating SCAD penalty and deep neural network.
result The model effectively selects important texture features and estimates nonparametric components.
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.
Open dataset for machine learning with reduced high-angle artefacts.
problem High-angle artefacts in cone-beam CT data for machine learning.
method Open data collection of 42 walnuts with varied cone angles, combined for artefact reduction.
result Ground truth images from combined data for supervised learning.
The development of computed tomography (CT) image reconstruction methods that significantly reduce patient radiation exposure while maintaining high image quality is an important area of research in low-dose CT (LDCT) imaging. We propose a new penalized weighted least squares (PWLS) reconstruction method that exploits …
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.
AI tool automates blood segmentation from head CT scans after SAH.
problem Accurate volumetric assessment of SAH patients for clinical and prognostic implications.
method Transformer-based Swin UNETR architecture for noncontrast CT scans.
result High accuracy and robust performance across internal and external validation cohorts.
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.
V-Net speeds up brain tumor segmentation in MRI scans.
problem Manual tumor segmentation is time-consuming and inaccurate.
method Applied a volumetric, fully convolutional neural network (V-Net) to MRI scans.
result Achieved a whole tumor dice score of 0.89.
Optimizes ASL-MRF scan design for precise brain hemodynamics quantification.
problem Fixing model parameters in ASL introduces bias, and multiparametric estimation degrades precision.
method Optimizes ASL labeling durations using Cramer-Rao Lower Bound (CRLB) and proposes a neural network regression framework.
result Improved precision in estimating multiple hemodynamic parameters from a single scan.
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.
3D CNN accurately classifies infant neurodevelopmental age from MRI scans.
problem Estimating neurodevelopmental age in infants from MRI data.
method 3D Convolutional Neural Network (3D CNN) trained on MRI images of 112 infants.
result 3D CNN achieves 99% sensitivity and 98.3% specificity in age classification.
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.
BrainCast predicts whole-brain fMRI time series from short scans.
problem Short scans reduce fMRI data quality and statistical power.
method Spatio-temporal forecasting framework for fMRI time series.
result BrainCast improves fMRI time series quality and prediction.
This study assesses ML methods for brain tumor segmentation and survival prediction.
problem Segmenting and predicting outcomes of brain tumors with varying sub-regions and heterogeneous properties.
method Evaluation of state-of-the-art machine learning algorithms on BraTS challenge datasets.
result Identification of best ML algorithms for brain tumor segmentation and survival prediction.
Proposes a strategy to train models with minimal labeled data.
problem Scarce and expensive labeled data for medical tasks.
method Recursive training strategy to use image-level annotations for pixel-level segmentation.
result Improved segmentation of intracranial hemorrhage in CT scans.
Machine learning models for COVID-19 detection and prognosis from chest images are flawed and unreliable.
problem Developing reliable machine learning models for COVID-19 diagnosis and prognosis from chest images.
method Systematic review of machine learning models published in 2020.
result None of the models identified are of clinical use due to methodological flaws and biases.
This paper predicts registration error in medical images using a random regression forest.
problem Predicting registration error in medical images without a ground truth.
method Random regression forest trained on features related to transformation model and dissimilarity after registration.
result The method achieves good performance in automatic quality control of large-scale image analysis.
Paper develops a model to identify LVO in stroke patients.
problem Early identification of LVO in stroke patients to prevent severe outcomes.
method Used demographic, clinical, and CT scan data to build three hierarchical models.
result Level-3 model with clinical and imaging features achieved best performance.
Deep learning and radiomics methods assess coronary artery plaque from CT scans.
problem Improving patient management and clinical outcomes by assessing coronary artery plaque.
method Three machine learning approaches: radiomics, deep learning, and fusion of both.
result Methods achieve AUC scores of 0.84-0.88, comparable to FFR measurements.
Computer science scans LLMs to understand and manipulate their economic forecasts.
problem Understanding and controlling the reasoning of large language models in economics.
method Brain scanning techniques applied to LLMs to identify and manipulate underlying concepts.
result LLMs can be steered to generate forecasts with specific biases, allowing for correction or simulation.
System accurately detects lung cancer from CT images.
problem Early and accurate detection of lung cancer.
method Developed algorithms using a dataset of CT images.
result Accuracy of 72.2% on test dataset.
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.
Self-supervised method estimates depth from monocular endoscopy videos.
problem Depth estimation from monocular endoscopy data without manual labeling.
method Convolutional neural networks trained with sparse supervision from stereo methods.
result Submillimeter mean residual error in cross-patient CT scans comparison.
A CNN on semi-regular meshes classifies brain diseases from MRI scans.
problem Classifying brain diseases from MRI scans.
method Developed a vertex-based graph CNN for semi-regular triangulated meshes.
result Vertex-based graph CNN outperformed spectral graph CNN in classifying MCI and AD.
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.
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.
3D ConvNets improved with Project & Excite for medical imaging segmentation.
problem Improving segmentation performance in 3D medical imaging.
method Proposed Project & Excite (PE) modules for 3D F-CNNs, extending 2D recalibration methods.
result Project & Excite modules boost segmentation performance up to 0.3 in Dice Score.
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.
New method reconstructs CT images from limited angles using neural networks.
problem High artifact reconstructions from limited angle CT scans.
method Implicit sinogram completion with 1D and 2D CNNs.
result Combined strategy outperforms competitive baselines.