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Found 7 papers, 5 papers with code
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification
Deep learning now enables automatic and robust extraction of cardiac function descriptors from echocardiographic sequences, such as ejection fraction or strain.
Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?
Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by experts (CAMUS being one of the largest public databases).
Echocardiography Segmentation with Enforced Temporal Consistency
In this paper, we propose a framework to learn the 2D+time apical long-axis cardiac shape such that the segmented sequences can benefit from temporal and anatomical consistency constraints.
Neural Teleportation
In this paper, we explore a process called neural teleportation, a mathematical consequence of applying quiver representation theory to neural networks.
Cardiac Segmentation with Strong Anatomical Guarantees
In this paper, we present a framework for producing cardiac image segmentation maps that are guaranteed to respect pre-defined anatomical criteria, while remaining within the inter-expert variability.
On the effectiveness of GAN generated cardiac MRIs for segmentation
On one side of our model is a Variational Autoencoder (VAE) trained to learn the latent representations of cardiac shapes.
Cardiac MRI Segmentation with Strong Anatomical Guarantees
In this paper, we propose a cardiac MRI segmentation method which always produces anatomically plausible results.
