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.
Active label refinement for robust training of imbalanced med- ical image classification tasks in the presence of high label noise
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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.