Label dropout mitigates shortcut learning in multi-dataset partially labelled echocardiography segmentation, improving Dice scores by 62% and 25% on two cardiac structures.
The Lancet Digital Health4(1), e46– e54 (Jan 2022)
2 Pith papers cite this work, alongside 176 external citations. Polarity classification is still indexing.
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CISR-Net achieves SOTA echocardiography segmentation by fusing local transition probability correlations for semantic rectification and frequency-domain denoising pre-training.
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Label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets With Domain Shift and Partial Labelling
Label dropout mitigates shortcut learning in multi-dataset partially labelled echocardiography segmentation, improving Dice scores by 62% and 25% on two cardiac structures.
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Automatic Echocardiography Segmentation via Transition Probability Correlation for Stable Semantic Extraction
CISR-Net achieves SOTA echocardiography segmentation by fusing local transition probability correlations for semantic rectification and frequency-domain denoising pre-training.