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Found 7 papers, 4 papers with code
EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks
In this work, we introduce EchoGNN, a model based on graph neural networks (GNNs) to estimate EF from echo videos.
Class Impression for Data-free Incremental Learning
Here, we propose a novel data-free class incremental learning framework that first synthesizes data from the model trained on previous classes to generate a \ours.
Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography
In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements.
U-LanD: Uncertainty-Driven Video Landmark Detection
We tackle a specifically challenging problem, where training labels are noisy and highly sparse.
Reciprocal Landmark Detection and Tracking with Extremely Few Annotations
The model is trained using few annotated frames across the entire cardiac cine sequence to generate consistent detection and tracking of landmarks, and an adversarial training for the model is proposed to take advantage of these annotated frames.
GAN-enhanced Conditional Echocardiogram Generation
One of the most sought-after problems in echo is the segmentation of cardiac structures (e. g. chambers).
On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra-observer Variability in 2D Echocardiography Quality Assessment
Nevertheless, the observer variability in the expert's assessment can impact the quality quantification accuracy.
