Search Results for author:
Found 12 papers, 3 papers with code
Voxel2Hemodynamics: An End-to-end Deep Learning Method for Predicting Coronary Artery Hemodynamics
In this work, we proposed an end-to-end deep learning framework, which could predict the coronary artery hemodynamics from CCTA images.
Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Network
3) A dataset named CCA-200 is collected, consisting of 200 CCTA images with coronary artery disease.
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection
This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset.
Improving Sample Efficiency of Deep Learning Models in Electricity Market
The superior performance of deep learning relies heavily on a large collection of sample data, but the data insufficiency problem turns out to be relatively common in global electricity markets.
Towards Self-supervised and Weight-preserving Neural Architecture Search
In this work, we propose the self-supervised and weight-preserving neural architecture search (SSWP-NAS) as an extension of the current NAS framework by allowing the self-supervision and retaining the concomitant weights discovered during the search stage.
Contrastive and Selective Hidden Embeddings for Medical Image Segmentation
Medical image segmentation has been widely recognized as a pivot procedure for clinical diagnosis, analysis, and treatment planning.
Open-Access Data and Toolbox for Tracking COVID-19 Impact on Power Systems
Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation.
Estimating Demand Flexibility Using Siamese LSTM Neural Networks
There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices.
CRT-Net: A Generalized and Scalable Framework for the Computer-Aided Diagnosis of Electrocardiogram Signals
Subsequently, a novel deep neural network, namely CRT-Net, is designed for the fine-grained and comprehensive representation and recognition of 1-D ECG signals.
Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation
In this paper, we aim to boost the performance of semi-supervised learning for medical image segmentation with limited labels using a self-ensembling contrastive learning technique.
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization
To this end, we have developed SenseCare research platform, which is designed to facilitate translational research on intelligent diagnosis and treatment planning in various clinical scenarios.
Human-Computer Interaction Image and Video Processing
