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Found 6 papers, 0 papers with code
Cardiac valve event timing in echocardiography using deep learning and triplane recordings
On an external independent test consisting of apical long-axis data from 180 other patients, the worst performing event detection had an aFD of 1. 8 (30 ms).
Deep Learning for Multi-Level Detection and Localization of Myocardial Scars Based on Regional Strain Validated on Virtual Patients
In this work, we propose a single framework to predict myocardial disease substrates at global, territorial, and segmental levels using regional myocardial strain traces as input to a convolutional neural network (CNN)-based classification algorithm.
A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN
A quantitative analysis of the 3D segmentations given by the models trained with the synthetic images indicated the potential use of this GAN approach to generate 3D synthetic data, use the data to train DL models for different clinical tasks, and therefore tackle the problem of scarcity of 3D labeled echocardiography datasets.
Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach
Although deep learning techniques have been successful in achieving this, they still struggle with fully verifying the suitability of an image for specific measurements due to factors like the correct location, pose, and potential occlusions of cardiac structures.
Doppler Spectrum Classification with CNNs via Heatmap Location Encoding and a Multi-head Output Layer
We analyze example images that fall outside of our proposed classes to show our confidence metric can prevent many misclassifications.
Automated Left Ventricle Dimension Measurement in 2D Cardiac Ultrasound via an Anatomically Meaningful CNN Approach
Treating the problem as a landmark detection problem, we propose a modified U-Net CNN architecture to generate heatmaps of likely coordinate locations.
