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Found 33 papers, 4 papers with code
MsMorph: An Unsupervised pyramid learning network for brain image registration
In the field of medical image analysis, image registration is a crucial technique.
SGUQ: Staged Graph Convolution Neural Network for Alzheimer's Disease Diagnosis using Multi-Omics Data
Experimental results indicate that 46. 23% of the samples can be reliably predicted using only single-modal omics data (mRNA), while an additional 16. 04% of the samples can achieve reliable predictions when combining two omics data types (mRNA + DNA methylation).
A Complex-Coefficient Voltage Control for Virtual Synchronous Generators for Dynamic Enhancement and Power-Voltage Decoupling
As electric power systems evolve towards decarbonization, the transition to inverter-based resources (IBRs) presents challenges to grid stability, necessitating innovative control solutions.
A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture
Furthermore, the staged model suggested that 54. 49% of patients did not require DXA scanning.
Multi-graph Graph Matching for Coronary Artery Semantic Labeling
The MGM algorithm assesses the similarity between arterials in multiple vascular tree graphs, considering the cycle consistency between each pair of graphs.
Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA
Fourthly, we employed ICP, SICP or CPD algorithm to achieve a fine registration for the point clouds (together with the special points of APIGs) of the LV epicardial surfaces (LVERs) in SPECT and CTA images.
3D Lymphoma Segmentation on PET/CT Images via Multi-Scale Information Fusion with Cross-Attention
Additionally, a Pearson correlation coefficient of 0. 91 and an R^2 of 0. 89 were observed when comparing manual annotations to segmentation results for TMTV measurement.
A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images
By using five-fold cross-validation, a deep learning model incorporating uncertainty estimation was developed to classify RCC subtypes into clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC).
Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics
By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information.
A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification
Uncertainty quantification from Ensemble 1 allowed for multi-stage decision-making to determine if the acquisition of SPECT data for a patient is necessary.
Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling
Coronary artery disease (CAD) is one of the primary causes leading to death worldwide.
Automated identification and quantification of myocardial inflammatory infiltration in digital histological images to diagnose myocarditis
This study aims to develop a new computational pathology approach that automates the identification and quantification of myocardial inflammatory infiltration in digital HE-stained images to provide a quantitative histological diagnosis of myocarditis. 898 HE-stained whole slide images (WSIs) of myocardium from 154 heart transplant patients diagnosed with myocarditis or dilated cardiomyopathy (DCM) were included in this study.
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation
However, there is a problem of domain generalization (DG) in the actual de-ployment, that is, the performance of the model trained by FL in unseen domains will decrease.
A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy
The model was fine-tuned to extract relevant features from the ECG images, and then tested on our dataset of CRT patients to predict their response.
Coronary Artery Semantic Labeling using Edge Attention Graph Matching Network
The presence of atherosclerotic lesions in coronary arteries is the underlying pathophysiological basis of CAD, and accurate extraction of individual arterial branches using invasive coronary angiography (ICA) is crucial for stenosis detection and CAD diagnosis.
A new method using deep learning to predict the response to cardiac resynchronization therapy
The DL model outperformed the ML models, showcasing the additional predictive benefit of utilizing SPECT MPI polarmaps.
CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data
Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data.
Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection
For severity detection, the hybrid model with radiomics features of both lungs and EAT showed improvements in AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) compared to the model with only lung radiomics features.
AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms
Semantic labeling of coronary arterial segments in invasive coronary angiography (ICA) is important for automated assessment and report generation of coronary artery stenosis in the computer-aided diagnosis of coronary artery disease (CAD).
Multi-view information fusion using multi-view variational autoencoders to predict proximal femoral strength
The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion.
A New Hip Fracture Risk Index Derived from FEA-Computed Proximal Femur Fracture Loads and Energies-to-Failure
The results showed that the average of the area under the receive operating characteristic curve (AUC) using PC1 was always higher than that using all FE parameters combined in the male subjects.
Automatic reorientation by deep learning to generate short axis SPECT myocardial perfusion images
Five-fold cross-validation with 180 stress and 201 rest MPIs was used for training and internal validation; the remaining images were used for testing.
Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms
A deep learning model U-Net 3+, which incorporates the full-scale skip connections and deep supervisions, was proposed for automatic extraction of coronary arteries from ICAs.
A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images
Conclusion: Our proposed method achieved a high accuracy in extracting LV myocardial contours and assessing LV function.
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation
A common problem in both pruning and distillation is to determine compressed architecture, i. e., the exact number of filters per layer and layer configuration, in order to preserve most of the original model capacity.
Spatial-temporal V-Net for automatic segmentation and quantification of right ventricles in gated myocardial perfusion SPECT images
By integrating the spatial features from each cardiac frame of the gated MPS and the temporal features from the sequential cardiac frames of the gated MPS, we developed a Spatial-Temporal V-Net (ST-VNet) for automatic extraction of RV endocardial and epicardial contours.
Automatic Identification of the End-Diastolic and End-Systolic Cardiac Frames from Invasive Coronary Angiography Videos
More specifically, a detection algorithm is first used to detect the key points of coronary arteries, and then an optical flow method is employed to track the trajectories of the selected key points.
A method using deep learning to discover new predictors of CRT response from mechanical dyssynchrony on gated SPECT MPI
Correlation analysis was used to explain the relationships between new parameters with conventional LVMD parameters.
A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images
In this paper, we aim to develop a deep learning-based method using an encoder-decoder deep architecture to automatically extract both lumen and MA border.
A Deep Learning-Based Approach to Extracting Periosteal and Endosteal Contours of Proximal Femur in Quantitative CT Images
In this study, we proposed an approach based on deep learning for the automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments.
A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms
We aim to develop an automatic algorithm by deep learning to extract coronary arteries from ICAs. In this study, a multi-input and multi-scale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation.
A Novel Method for ECG Signal Classification via One-Dimensional Convolutional Neural Network
This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals.
A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images
During the experiments for the entire cohort then for male and female subjects separately, 90% of the subjects were used in 10-fold cross-validation for training and internal validation, and to select the optimal parameters of the proposed models; the rest of the subjects were used to evaluate the performance of models.
