Abubakar Abid commited on
Commit
5d25464
1 Parent(s): a99aee1

Update app.py

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  1. app.py +0 -1
app.py CHANGED
@@ -87,6 +87,5 @@ title = None #"Left Ventricle Segmentation"
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  description = "This semantic segmentation model identifies the left ventricle in echocardiogram images."
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  # videos. Accurate evaluation of the motion and size of the left ventricle is crucial for the assessment of cardiac function and ejection fraction. In this interface, the user inputs apical-4-chamber images from echocardiography videos and the model will output a prediction of the localization of the left ventricle in blue. This model was trained on the publicly released EchoNet-Dynamic dataset of 10k echocardiogram videos with 20k expert annotations of the left ventricle and published as part of ‘Video-based AI for beat-to-beat assessment of cardiac function’ by Ouyang et al. in Nature, 2020."
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  thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-echonet/master/thumbnail.png"
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- css = ".footer {display:none !important}"
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  gr.Interface(segment, i, o, examples=examples, css=css, allow_flagging=False, analytics_enabled=False,
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  title=title, description=description, thumbnail=thumbnail).launch()
 
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  description = "This semantic segmentation model identifies the left ventricle in echocardiogram images."
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  # videos. Accurate evaluation of the motion and size of the left ventricle is crucial for the assessment of cardiac function and ejection fraction. In this interface, the user inputs apical-4-chamber images from echocardiography videos and the model will output a prediction of the localization of the left ventricle in blue. This model was trained on the publicly released EchoNet-Dynamic dataset of 10k echocardiogram videos with 20k expert annotations of the left ventricle and published as part of ‘Video-based AI for beat-to-beat assessment of cardiac function’ by Ouyang et al. in Nature, 2020."
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  thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-echonet/master/thumbnail.png"
 
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  gr.Interface(segment, i, o, examples=examples, css=css, allow_flagging=False, analytics_enabled=False,
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  title=title, description=description, thumbnail=thumbnail).launch()