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Found 5 papers, 1 papers with code
AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography
In this work we propose for the first time an AI approach for deriving advanced biomarkers of systolic and diastolic LV function from 2-D echocardiography based on segmentations of the full cardiac cycle.
Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction
Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthcare.
A Multimodal Deep Learning Model for Cardiac Resynchronisation Therapy Response Prediction
Next, a multimodal deep learning classifier is used for CRT response prediction, which combines the latent spaces of the segmentation models of the two modalities.
A Framework for Auditing Data Center Energy Usage and Mitigating Environmental Footprint
As the Data Science field continues to mature, and we collect more data, the demand to store and analyze them will continue to increase.
Distributed, Parallel, and Cluster Computing
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction
Our key contribution is that the VAE disentangles the latent space based on `explanations' drawn from existing clinical knowledge.
