frances-dean commited on
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5fa2298
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Update app.py

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  1. app.py +3 -4
app.py CHANGED
@@ -502,11 +502,10 @@ def generate_example():
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  title = "Physics-informed self-supervised learning for predicting cardiac digital twins with echocardiography"
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  description = """
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- <p style='text-align: center'> Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang, Anthony Philippakis, David Sontag, Ahmed Alaa <br>
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- Check out our <a href='https://github.com/AlaaLab/CardioPINN' target='_blank'>code.</a></p>
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- <p> We develop methodology for predicting digital twins from non-invasive cardiac ultrasound images in <a href='https://arxiv.org/abs/2403.00177'>Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning</a>. \n \n
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  We demonstrate the ability of our model to predict left ventricular pressure-volume loops using image data here. To run example predictions on samples from the <a href='https://echonet.github.io/dynamic/'>EchoNet</a> dataset, click the first button. \n \n
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- Below you can input values of predicted parameters and output a simulated pressure-volume loop predicted by the <a href='https://ieeexplore.ieee.org/document/4729737/keywords#keywords'>Simaan et al 2008</a> hydraulic analogy model by pressuing 'Run simulation.'</p>
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  """
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  gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>" + title + "</h1>")
 
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  title = "Physics-informed self-supervised learning for predicting cardiac digital twins with echocardiography"
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  description = """
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+ <p style='text-align: center'> Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang, Anthony Philippakis, David Sontag, Ahmed Alaa <br></p>
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+ <p> We develop methodology for predicting digital twins from non-invasive cardiac ultrasound images in <a href='https://arxiv.org/abs/2403.00177'>Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning</a>. Check out our <a href='https://github.com/AlaaLab/CardioPINN' target='_blank'>code.</a> \n \n
 
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  We demonstrate the ability of our model to predict left ventricular pressure-volume loops using image data here. To run example predictions on samples from the <a href='https://echonet.github.io/dynamic/'>EchoNet</a> dataset, click the first button. \n \n
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+ Below you can input values of predicted parameters and output a simulated pressure-volume loop predicted by the <a href='https://ieeexplore.ieee.org/document/4729737/keywords#keywords'>Simaan et al 2008</a> hydraulic analogy model by pressing 'Run simulation.'</p>
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  """
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  gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>" + title + "</h1>")