Variance of Gradients effectively flags erroneous ground truth labels during echocardiography segmentation training, and refurbishing them with pseudo-labeling boosts performance especially under high error rates on the CAMUS dataset.
Automated in- terpretation of systolic and diastolic function on the echocardiogram: a multicohort study
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Detecting and refurbishing ground truth errors during training of deep learning-based echocardiography segmentation models
Variance of Gradients effectively flags erroneous ground truth labels during echocardiography segmentation training, and refurbishing them with pseudo-labeling boosts performance especially under high error rates on the CAMUS dataset.